diff --git a/docs/source/_static/JOINT_DAG.png b/docs/source/_static/JOINT_DAG.png new file mode 100644 index 00000000..4ce8f04e Binary files /dev/null and b/docs/source/_static/JOINT_DAG.png differ diff --git a/docs/source/_static/forwards_backwards.png b/docs/source/_static/forwards_backwards.png new file mode 100644 index 00000000..dbaaf71e Binary files /dev/null and b/docs/source/_static/forwards_backwards.png differ diff --git a/docs/source/_static/potential_outcomes.png b/docs/source/_static/potential_outcomes.png new file mode 100644 index 00000000..f8c9e2f7 Binary files /dev/null and b/docs/source/_static/potential_outcomes.png differ diff --git a/docs/source/_static/probabilistic_intervention_fix.png b/docs/source/_static/probabilistic_intervention_fix.png new file mode 100644 index 00000000..550b23ac Binary files /dev/null and b/docs/source/_static/probabilistic_intervention_fix.png differ diff --git a/docs/source/knowledgebase/glossary.rst b/docs/source/knowledgebase/glossary.rst index 1f33df2e..4d971590 100644 --- a/docs/source/knowledgebase/glossary.rst +++ b/docs/source/knowledgebase/glossary.rst @@ -66,6 +66,9 @@ Glossary Pretest-posttest design A quasi-experimental design where the treatment effect is estimated by comparing an outcome measure before and after treatment. + Probabilistic Programming + Probabilistic programming is the practice of expressing statistical using general-purpose programming languages extended with constructs for random variables, probability distributions, and inference. Prominent examples are `PyMC` and `Stan` + Propensity scores An estimate of the probability of adopting a treatment status. Used in re-weighting schemes to balance observational data. diff --git a/docs/source/knowledgebase/index.md b/docs/source/knowledgebase/index.md index 94a573c0..1a253cf1 100644 --- a/docs/source/knowledgebase/index.md +++ b/docs/source/knowledgebase/index.md @@ -6,6 +6,7 @@ glossary design_notation quasi_dags.ipynb +structural_causal_models.ipynb causal_video_resources causal_written_resources ::: diff --git a/docs/source/knowledgebase/structural_causal_models.ipynb b/docs/source/knowledgebase/structural_causal_models.ipynb new file mode 100644 index 00000000..4d5c4eb1 --- /dev/null +++ b/docs/source/knowledgebase/structural_causal_models.ipynb @@ -0,0 +1,5617 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian Structural Causal Inference" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], + "source": [ + "import warnings\n", + "\n", + "import arviz as az\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pymc as pm\n", + "import pymc_bart as pmb\n", + "import pytensor.tensor as pt\n", + "import statsmodels.formula.api as smf\n", + "from matplotlib import pyplot as plt\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "When we ask \"What is the effect of a medical treatment?\" or \"Does quitting smoking cause weight gain?\" or \"Do job training programs increase earnings?\", we are not simply asking about the treatment itself. We are asking: What world are we operating in? This perspective is more easily seen if you imagine a causal analyst as a pet-shop owner introducing a new fish to one of their many acquariums. The new fish's survival and behavior depend less on its intrinsic properties than on how it fits within this complex, interconnected system of PH balances and predators. In which tank will the new fish thrive? \n", + "\n", + "Different causal methods make different choices about how much of this system to model explicitly. Some methods succeed by not modeling the full system: instrumental variables isolate causal effects through credible exclusion restrictions; difference-in-differences leverages parallel trends; interrupted time-series assumes stationarity. These design-based approaches gain power by minimizing modeling assumptions about the data-generating process. See {cite:t}`pearl2000causality` or {cite:t}`angrist2009mostly` for more detailed distinctions. The unifying thread between these diverse methods is the idea of a causal model as a _probabilistic program_ : an inferential routine designed to explicitly yield insights into the effect of some intervention or treatment on the system of interest. Whether design based or model-based, causal inference methods assume a data generating process - the distinction between these methods is how explicitly the system is rendered.\n", + "\n", + "#### Modelling Worlds and Counterfactual Worlds\n", + "\n", + "Bayesian structural modeling attempts to parameterize the system itself. Where design-based methods answer \"what is the causal effect under these identification assumptions?\", structural models ask \"what is the most plausible data-generating process, and how do interventions propagate through it?\". In Bayesian structural causal inference the focus is slightly different in that we wish to model both the treatment and the environment i.e. the fish and the fishtank. The trade-off is transparency for complexity. You must specify more of the data-generating process, which creates more opportunities for model misspecification. But every assumption becomes an explicit, testable model component rather than an implicit background condition.\n", + "\n", + "This is a two step move in the Bayesian paradigm. First we infer \"backwards\" what is the most plausible state of the world $w$ conditioned on the observable data. The \"world\" of the model is defined by: (1) a causal graph relating variables, (2) likelihood functions specifying how each variable depends on its causes, and (3) prior distributions over parameters. Optionally, this may include latent confounders, measurement models, and selection mechanisms—each adding structural detail but also complexity. With this world in place, we continue to assess the probabilistic predictive distribution of treatment and outcome at the plausible range of counterfactual worlds. \n", + "\n", + "![](../_static/forwards_backwards.png)\n", + "\n", + "The important point is that we characterise the plausible worlds by how much structure we learn about in the model specification. The more structure we seek to infer, the more we risk model misspecification, but simultaneously, the more structure we learn the more useful and transparent our conclusions. This structural commitment contrasts sharply with reduced-form approaches that minimize explicit modeling.\n", + "\n", + "#### Minimalism and Structural Maximalism\n", + "\n", + "The term \"reduced form\" originates from econometric simultaneous equations models. Early economists wanted to model supply and demand as functions of price, but faced a problem: quantities also determine price in competitive markets. Because these structural relationships are mutually determined, the system is hard to solve directly. The solution was algebraic transformation: solve for the 'reduced form' that expresses endogenous variables purely as functions of exogenous ones.\n", + "\n", + "Reduced form systems are transformed systems of interest designed to estimate the focal parameters by leveraging observable and tractable data. These approaches eschew \"theory driven\" model specifications in favour of models with precise _identifiable estimands_. This approach - transforming complex structural systems into tractable estimating equations - reflects a broader methodological commitment. It is for this minimalist preference that they are typically contrasted with structural models that aim to express the \"fuller\" data generating process. Design based causal inference methods typically adopt this focus on identifiability within a regression framework. For richer discussion in this vein see {cite:t}`hansenEconometrics` or {cite:t}`aronowFoundations`. \n", + "\n", + "When we regress an outcome $Y$ on a treatment $T$ and a set of covariates $X$,\n", + "\n", + "$$Y = \\alpha T + X \\beta + \\epsilon$$\n", + "\n", + "the coefficient $\\alpha$ captures the average change in Y associated with a one-unit change in $T$. Only under strong assumptions, however, can we interpret this as a causal effect. In real-world settings, those assumptions (like exogeneity of $T$) are fragile:\n", + "\n", + "- Confounding: Unobserved or omitted variables affect both \n", + "$T$ and $Y$.\n", + "\n", + "- Endogeneity: Treatment assignment mechanisms are correlated with the error term.\n", + "\n", + "- Measurement uncertainty: Model parameters and predictions have uncertainty not captured by point estimates.\n", + "\n", + "The innovative methods of inference (like Two-stage least squares, propensity score weighting or DiD designs) that came to define the _credibility revolution_ in the social sciences, seek to overcome this risk of confounding with constraints or assumptions to bolster identification of the causal parameters. See See {cite:t}`angrist2009mostly`. Bayesian probabilistic causal inference addresses these challenges by explicitly modelling the data-generating process and quantifying all sources of uncertainty. Rather than point estimates and design assumptions, we infer full posterior distributions over causal parameters and even over counterfactual outcomes. Rather than isolating the outcome equation from the treatment equation, we model them together as parts of a single generative system. This approach mirrors how interventions occur in the real world. The propensity for adopting a treatment can be predicted by the same factors which determine treatment outcomes. This structure creates the risk of confounding because the efficacy of the treatment is obscured by the influence of these shared predictors. When we fit such a model, we learn about every component simultaneously—the effect of the treatment, the influence of confounders, and the uncertainty that ties them together. Once fitted, Bayesian models can generate posterior predictive draws for “what if” scenarios. This capacity lets us compute causal estimands like the ATE or individual treatment effects directly from the posterior.\n", + "\n", + "In this tutorial, we’ll move step by step from data simulation to Structural Bayesian Causal models:\n", + "\n", + ":::{admonition} The Structure of the Document\n", + ":class: tip\n", + "\n", + "- Simulate data with known causal structure (including confounding and exclusion restrictions).\n", + "\n", + "- Fit and interpret Bayesian models for continuous treatments.\n", + "\n", + "- Extend to binary treatments and potential outcomes.\n", + "\n", + "- Use posterior predictive imputation to simulate counterfactuals.\n", + "\n", + "- Demonstrate the relationship between the structural modelling perspective with the potential outcomes framework.\n", + "\n", + "- Apply the model to an empircal example with parameter recovery checks and sensitivity analysis\n", + ":::\n", + "\n", + "\n", + "This approach will show how Bayesian methods provide a unified and transparent lens on causal inference. We will cover estimation, identification, and uncertainty in a single coherent framework. The goal is to demonstrate how model comparison and sensitivity analysis, core components of the contemporary Bayesian workflow, are uniquely suited to causal inference. By treating our models as probabilistic programs we can interrogate each under different assumptions, priors and specifications. The Bayesian framework of joint structural modelling makes transparent what most causal analyses leave implicit: which conclusions follow from data, and which follow from structural commitments.\n", + "\n", + "### Simulating the Source of Truth\n", + "\n", + "Every causal claim rests on untestable assumptions about the data-generating process. Before we can trust our methods in the wild, we must test them in controlled conditions where truth is known. The simulation below constructs such a laboratory: we specify the causal structure explicitly, introduce confounding deliberately, and then ask whether our Bayesian models recover what we seeded in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(123)\n", + "\n", + "\n", + "def inv_logit(z):\n", + " \"\"\"Compute the inverse logit (sigmoid) of z.\"\"\"\n", + " return 1 / (1 + np.exp(-z))\n", + "\n", + "\n", + "def standardize_df(df, cols):\n", + " means = df[cols].mean()\n", + " sds = df[cols].std(ddof=1)\n", + " df_s = (df[cols] - means) / sds\n", + " return df_s, means, sds\n", + "\n", + "\n", + "def simulate_data(n=2500, alpha_true=3.0, rho=0.6, cate_estimation=False):\n", + " # Exclusion restrictions:\n", + " # X[0], X[1] affect both Y and T (confounders)\n", + " # X[2], X[3] affect ONLY T (instruments for T)\n", + " # X[4] affects ONLY Y (predictor of Y only)\n", + "\n", + " betaY = np.array([0.5, -0.3, 0.0, 0.0, 0.4, 0, 0, 0, 0]) # X[2], X[3] excluded\n", + " betaD = np.array([0.7, 0.1, -0.4, 0.3, 0.0, 0, 0, 0, 0]) # X[4] excluded\n", + " p = len(betaY)\n", + "\n", + " # noise variances and correlation\n", + " sigma_U = 3.0\n", + " sigma_V = 3.0\n", + "\n", + " # design matrix (n × p) with mean-zero columns\n", + " X = np.random.normal(size=(n, p))\n", + " X = (X - X.mean(axis=0)) / X.std(axis=0)\n", + "\n", + " mean = [0, 0]\n", + " cov = [[sigma_U**2, rho * sigma_U * sigma_V], [rho * sigma_U * sigma_V, sigma_V**2]]\n", + " errors = np.random.multivariate_normal(mean, cov, size=n)\n", + " U = errors[:, 0] # error in outcome equation\n", + " V = errors[:, 1] #\n", + "\n", + " # continuous treatment\n", + " T_cont = X @ betaD + V\n", + "\n", + " # latent variable for binary treatment\n", + " T_latent = X @ betaD + V\n", + " T_bin = np.random.binomial(n=1, p=inv_logit(T_latent), size=n)\n", + "\n", + " alpha_individual = 3.0 + 2.5 * X[:, 0]\n", + "\n", + " # outcomes\n", + " Y_cont = alpha_true * T_cont + X @ betaY + U\n", + " if cate_estimation:\n", + " Y_bin = alpha_individual * T_bin + X @ betaY + U\n", + " else:\n", + " Y_bin = alpha_true * T_bin + X @ betaY + U\n", + "\n", + " # combine into DataFrame\n", + " data = pd.DataFrame(\n", + " {\n", + " \"Y_cont\": Y_cont,\n", + " \"Y_bin\": Y_bin,\n", + " \"T_cont\": T_cont,\n", + " \"T_bin\": T_bin,\n", + " }\n", + " )\n", + " data[\"alpha\"] = alpha_true + alpha_individual\n", + " for j in range(p):\n", + " data[f\"feature_{j}\"] = X[:, j]\n", + " data[\"Y_cont_scaled\"] = (data[\"Y_cont\"] - data[\"Y_cont\"].mean()) / data[\n", + " \"Y_cont\"\n", + " ].std(ddof=1)\n", + " data[\"Y_bin_scaled\"] = (data[\"Y_bin\"] - data[\"Y_bin\"].mean()) / data[\"Y_bin\"].std(\n", + " ddof=1\n", + " )\n", + " data[\"T_cont_scaled\"] = (data[\"T_cont\"] - data[\"T_cont\"].mean()) / data[\n", + " \"T_cont\"\n", + " ].std(ddof=1)\n", + " data[\"T_bin_scaled\"] = (data[\"T_bin\"] - data[\"T_bin\"].mean()) / data[\"T_bin\"].std(\n", + " ddof=1\n", + " )\n", + " return data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each simulated observation has a treatment $T$, an outcome $Y$, and a set of covariates $X$ with distinct causal roles. Two covariates influence both the treatment and the outcome—these are the confounders. Two others affect only the treatment and serve as valid instruments. A final covariate affects only the outcome. The treatment and outcome errors are drawn from a correlated bivariate normal distribution, introducing endogeneity through their correlation parameter $\\rho$. When $\\rho$ is low the treatment can be considered exogenous and standard regression should recover the correct effect; while naive estimates will be biased when $\\rho$ is high.\n", + "\n", + "#### Confounding Structure\n", + "\n", + "The function produces both continuous and binary versions of the treatment and the outcome. This dual design lets us explore two worlds side by side: one where the treatment is a continuous dosage, and another where it is a binary decision. In both cases, the true causal effect of the treatment on the outcome is set to three. Because we know the truth, we can evaluate how well our Bayesian models recover true parameters. Even here you can see that the \"structure\" we impose on the world is abstraction over the concrete mechanisms acting in the world. We bundle the idea of selecting into the treatment as potential for correlation between treatment and outcome. This is a convenient and tractable proxy of a range of concrete settings where there is a risk of selection effects in the real world. \n", + "\n", + "![](../_static/JOINT_DAG.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the simulation code and the diagram above we have allowed the treatment and outcome to be predicted by shared variables `X0` and `X1`. These alone are sufficient to induce confounding into the estimation of the treatment on the outcome. We have also allowed `X2`, `X3` are potentially viable instrumental variables for predicting the outcome purged of the confounding effects of `X0` and `X1`. The rest of the variables are either noise or an independent predictor of the outcome. \n", + "\n", + "Before introducing the Bayesian machinery, it’s worth revisiting what goes wrong with ordinary least squares when the treatment and outcome share unobserved causes. The following code performs a simple sensitivity experiment: we vary the correlation $\\rho$ between the unobserved treatment and outcome errors and examine how the estimated treatment effect changes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " treatment_effects_binary treatment_effects_continuous rho\n", + "0 -1.201719 2.000000 -1.000000\n", + "1 -0.351161 2.221169 -0.777778\n", + "2 0.766409 2.457946 -0.555556\n", + "3 1.644626 2.652292 -0.333333\n", + "4 2.712099 2.924260 -0.111111\n", + "5 3.441161 3.117569 0.111111\n", + "6 4.347816 3.327324 0.333333\n", + "7 5.243494 3.537410 0.555556\n", + "8 6.258062 3.782707 0.777778\n", + "9 7.248784 4.000000 1.000000" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = simulate_data(n=2500, alpha_true=3, rho=0.6)\n", + "features = [col for col in data.columns if \"feature\" in col]\n", + "\n", + "treatment_effects_binary = []\n", + "treatment_effects_continuous = []\n", + "df_params = {\n", + " \"treatment_effects_binary\": [],\n", + " \"treatment_effects_continuous\": [],\n", + " \"rho\": [],\n", + "}\n", + "formula_cont = \"Y_cont ~ T_cont + \" + \" + \".join(features)\n", + "formula_bin = \"Y_bin ~ T_bin + \" + \" + \".join(features)\n", + "for rho in np.linspace(-1, 1, 10):\n", + " data = simulate_data(n=2500, alpha_true=3, rho=rho)\n", + " model_cont = smf.ols(formula_cont, data=data).fit()\n", + " model_bin = smf.ols(formula_bin, data=data).fit()\n", + " df_params[\"treatment_effects_continuous\"].append(model_cont.params[\"T_cont\"])\n", + " df_params[\"treatment_effects_binary\"].append(model_bin.params[\"T_bin\"])\n", + " df_params[\"rho\"].append(rho)\n", + "\n", + "df_params = pd.DataFrame(df_params)\n", + "df_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This loop re-simulates the dataset ten times, each with a different value of $\\rho$, ranging from –1 to 1. For each dataset, it fits two OLS regressions: one for the continuous treatment, and another for the binary treatment, both controlling for all observed covariates. The estimated coefficient on the treatment variable `T_cont` or `T_bin`—represents what OLS believes to be the causal effect. By collecting these estimates in df_params, we can plot them against the true correlation to see how endogeneity distorts inference.\n", + "\n", + "When $\\rho = 0$ the treatment and outcome errors are independent, and OLS recovers the true causal effect of 3. But as $\\rho$ grows, the estimates drift away from the truth, sometimes dramatically. The direction of bias depends on the sign of the unobserved relationship. If hidden factors push both treatment and outcome the same way, OLS overstates the effect. If they act in opposite directions, it understates it. Even though we’ve controlled for all observed features, the unobserved correlation sneaks bias into our estimates." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(20, 6))\n", + "ax.plot(\n", + " df_params[\"rho\"], df_params[\"treatment_effects_binary\"], label=\"Binary Treatment\"\n", + ")\n", + "ax.plot(\n", + " df_params[\"rho\"],\n", + " df_params[\"treatment_effects_continuous\"],\n", + " label=\"Continuous Treatment\",\n", + ")\n", + "ax.axhline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n", + "ax.set_xlabel(\"rho \\n correlation parameter\")\n", + "ax.set_ylabel(\"Treatment Effect Estimate\")\n", + "ax.set_title(\"Treatment Effect Estimates with OLS \\n Under Confounding\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now move from diagnosing bias to building a model that can recover causal effects under controlled conditions. To keep things interpretable, we begin with the unconfounded case, where the treatment and outcome share no latent correlation ($\\rho=0$). This setting lets us isolate what a Bayesian structural model actually does before we expose it to the challenges of endogeneity.\n", + "\n", + "#### Joint Modelling and Prior Structure\n", + "\n", + "At the heart of our approach is joint modelling: instead of fitting separate regressions for treatment and outcome, we model them together as draws from a joint multivariate distribution. The treatment equation captures how covariates predict exposure, while the outcome equation captures how both treatment and covariates predict the response. By expressing them jointly, we retain the covariance structure between their errors—an essential ingredient for causal inference once we later introduce confounding.\n", + "\n", + "The model is built using PyMC and organized through the function `make_joint_model()`. Each version shares the same generative logic but differs in how the priors handle variable selection and identification. We can think of these as different “dial settings” for how strongly the model shrinks irrelevant coefficients or searches for valid instruments. Four prior configurations are explored:\n", + "\n", + "- A normal prior, weak regularization with no variable selection. If the model succeeds here, the causal structure is identified through the joint modeling alone.\n", + "\n", + "- A spike-and-slab prior, which aggressively prunes away variables unlikely to matter, allowing the model to discover which features are true confounders or instruments.\n", + "\n", + "- A horseshoe prior, offering continuous shrinkage that downweights noise while preserving large signals. This is a middle path that downweights weak predictors without forcing them exactly to zero.\n", + "\n", + "- An exclusion-restriction prior, explicitly encoding which variables are allowed to influence the treatment but not the outcome, mimicking an instrumental-variable design.\n", + "\n", + "Each prior embodies a different epistemological stance on how much structure the data can learn versus how much the analyst must impose. In the unconfounded case, the treatment and outcome errors are independent, so the joint model effectively decomposes into two connected regressions. The treatment effect $\\alpha$ then captures the causal impact of the treatment on the outcome, and under this setting, its posterior should center around the true value of 3. The goal is not to solve confounding yet but to show that when the world is simple and well-behaved, the Bayesian model recovers the truth just as OLS does—but with richer uncertainty quantification and a coherent probabilistic structure.\n", + "\n", + "The following code defines the model and instantiates it under several prior choices. The model’s graphical representation, produced by `pm.model_to_graphviz()`, visualizes its structure: covariates feed into both the treatment and the outcome equations, the treatment coefficient $\\alpha$ links them, and the two residuals \n", + "$U$ and $V$ are connected through a correlation parameter $\\rho$, which we can freely set to zero or more substantive values. These parameterisations offer us a way to derive insight from the structure of the causal system under study. \n", + "\n", + "### Fitting the Continuous Treatment Model\n", + "\n", + "In this next code block we articulate the joint model for the continuous outcome and continuous treatment variable. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusterbeta_outcome (9)\n", + "\n", + "beta_outcome (9)\n", + "\n", + "\n", + "cluster9\n", + "\n", + "9\n", + "\n", + "\n", + "clusterbeta_treatment (9)\n", + "\n", + "beta_treatment (9)\n", + "\n", + "\n", + "cluster2500 x 9\n", + "\n", + "2500 x 9\n", + "\n", + "\n", + "cluster2500 x 2\n", + "\n", + "2500 x 2\n", + "\n", + "\n", + "cluster2500\n", + "\n", + "2500\n", + "\n", + "\n", + "\n", + "upper\n", + "\n", + "upper\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "rho\n", + "\n", + "rho\n", + "~\n", + "Uniform\n", + "\n", + "\n", + "\n", + "upper->rho\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cov_YD\n", + "\n", + "cov_YD\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "pi_O\n", + "\n", + "pi_O\n", + "~\n", + "Beta\n", + "\n", + "\n", + "\n", + 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"beta_T->mu_treatment\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X_data\n", + "\n", + "X_data\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "X_data->mu_outcome\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X_data->mu_treatment\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "observed\n", + "\n", + "observed\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "likelihood->observed\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu_outcome->likelihood\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu_treatment->likelihood\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu_treatment->mu_outcome\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coords = {\n", + " \"beta_outcome\": [col for col in data.columns if \"feature\" in col],\n", + " \"beta_treatment\": [col for col in data.columns if \"feature\" in col],\n", + " \"obs\": range(data.shape[0]),\n", + " \"latent\": [\"U\", \"V\"],\n", + " \"sigmas_1\": [\"var_U\", \"cov_UV\"],\n", + " \"sigmas_2\": [\"cov_VU\", \"var_V\"],\n", + "}\n", + "\n", + "\n", + "def relaxed_bernoulli(name, p, temperature=0.1, dims=None):\n", + " u = pm.Uniform(name + \"_u\", 0, 1, dims=dims)\n", + " logit_p = pt.log(p) - pt.log(1 - p)\n", + " return pm.Deterministic(\n", + " name, pm.math.sigmoid((logit_p + pt.log(u) - pt.log(1 - u)) / temperature)\n", + " )\n", + "\n", + "\n", + "def make_spike_and_slab_beta():\n", + " # RELAXED SPIKE-AND-SLAB PRIORS for aggressive variable selection\n", + " pi_O = pm.Beta(\"pi_O\", alpha=2, beta=2)\n", + " beta_O_raw = pm.Normal(\"beta_O_raw\", mu=0, sigma=2, dims=\"beta_outcome\")\n", + " gamma_O = relaxed_bernoulli(\"gamma_O\", pi_O, temperature=0.1, dims=\"beta_outcome\")\n", + " beta_outcome = pm.Deterministic(\"beta_O\", gamma_O * beta_O_raw, dims=\"beta_outcome\")\n", + "\n", + " pi_T = pm.Beta(\"pi_T\", alpha=2, beta=2)\n", + " beta_T_raw = pm.Normal(\"beta_T_raw\", mu=0, sigma=2, dims=\"beta_treatment\")\n", + " gamma_T = relaxed_bernoulli(\"gamma_T\", pi_T, temperature=0.1, dims=\"beta_treatment\")\n", + " beta_treatment = pm.Deterministic(\n", + " \"beta_T\", gamma_T * beta_T_raw, dims=\"beta_treatment\"\n", + " )\n", + "\n", + " return beta_outcome, beta_treatment\n", + "\n", + "\n", + "def make_horseshoe_beta(tau0):\n", + " tau_O = pm.HalfStudentT(\"tau_O\", nu=3, sigma=tau0)\n", + " # Local shrinkage parameters (one per coefficient)\n", + " lambda_O = pm.HalfCauchy(\"lambda_O\", beta=1.0, dims=\"beta_outcome\")\n", + " # Regularized horseshoe: c² controls tail behavior\n", + " c2_O = pm.InverseGamma(\"c2_O\", alpha=2, beta=2)\n", + " lambda_tilde_O = pm.Deterministic(\n", + " \"lambda_tilde_O\",\n", + " pm.math.sqrt(c2_O * lambda_O**2 / (c2_O + tau_O**2 * lambda_O**2)),\n", + " dims=\"beta_outcome\",\n", + " )\n", + "\n", + " # Outcome coefficients with horseshoe prior\n", + " beta_O_raw = pm.Normal(\"beta_O_raw\", mu=0, sigma=1, dims=\"beta_outcome\")\n", + " beta_outcome = pm.Deterministic(\n", + " \"beta_O\", beta_O_raw * lambda_tilde_O * tau_O, dims=\"beta_outcome\"\n", + " )\n", + "\n", + " # Same for treatment equation\n", + " tau_T = pm.HalfStudentT(\"tau_T\", nu=3, sigma=tau0)\n", + " lambda_T = pm.HalfCauchy(\"lambda_T\", beta=1.0, dims=\"beta_treatment\")\n", + " c2_T = pm.InverseGamma(\"c2_T\", alpha=2, beta=2)\n", + " lambda_tilde_T = pm.Deterministic(\n", + " \"lambda_tilde_T\",\n", + " pm.math.sqrt(c2_T * lambda_T**2 / (c2_T + tau_T**2 * lambda_T**2)),\n", + " dims=\"beta_treatment\",\n", + " )\n", + "\n", + " beta_T_raw = pm.Normal(\"beta_T_raw\", mu=0, sigma=1, dims=\"beta_treatment\")\n", + " beta_treatment = pm.Deterministic(\n", + " \"beta_T\", beta_T_raw * lambda_tilde_T * tau_T, dims=\"beta_treatment\"\n", + " )\n", + "\n", + " return beta_outcome, beta_treatment\n", + "\n", + "\n", + "def make_joint_model(X, Y, T, coords, priors_type=\"normal\", priors={}):\n", + " p = X.shape[1]\n", + " p0 = 5.0 # pick an expected number of nonzero coeffs\n", + " sigma_est = 1.0\n", + "\n", + " tau0 = (p0 / (p - p0)) * (sigma_est / np.sqrt(X.shape[0]))\n", + "\n", + " with pm.Model(coords=coords) as dml_model:\n", + " spike_and_slab = priors_type == \"spike_and_slab\"\n", + " horseshoe = priors_type == \"horseshoe\"\n", + " exclusion_restriction = priors_type == \"exclusion_restriction\"\n", + " p = X.shape[1]\n", + "\n", + " if not priors:\n", + " priors = {\n", + " \"rho\": [-0.99, 0.99],\n", + " }\n", + "\n", + " if spike_and_slab:\n", + " beta_outcome, beta_treatment = make_spike_and_slab_beta()\n", + "\n", + " elif horseshoe:\n", + " beta_outcome, beta_treatment = make_horseshoe_beta(tau0)\n", + "\n", + " elif exclusion_restriction:\n", + " ### Ensuring that there is an instruments i.e. predictors of the treatment that\n", + " ### impact the outcome only through the treatment\n", + " beta_outcome = pm.Normal(\n", + " \"beta_O\",\n", + " 0,\n", + " [2.0, 2.0, 0.001, 0.001, 2.0, 2, 2, 2, 2],\n", + " dims=\"beta_outcome\",\n", + " )\n", + " beta_treatment = pm.Normal(\n", + " \"beta_T\",\n", + " 0,\n", + " [2.0, 2.0, 2.0, 2.0, 0.001, 2, 2, 2, 2],\n", + " dims=\"beta_treatment\",\n", + " )\n", + " else:\n", + " beta_outcome = pm.Normal(\"beta_O\", 0, 1, dims=\"beta_outcome\")\n", + " beta_treatment = pm.Normal(\"beta_T\", 0, 1, dims=\"beta_treatment\")\n", + "\n", + " X_data = pm.Data(\"X_data\", X.values)\n", + " observed_data = pm.Data(\"observed\", np.column_stack([Y.values, T.values]))\n", + "\n", + " alpha = pm.Normal(\"alpha\", mu=0, sigma=5)\n", + "\n", + " # Error standard deviations\n", + " sigma_U = pm.Exponential(\"sigma_U\", 1.0)\n", + " sigma_V = pm.Exponential(\"sigma_V\", 1.0)\n", + "\n", + " # Correlation between errors (confounding parameter)\n", + " m = pm.TruncatedNormal(\n", + " \"m\", mu=0, sigma=0.5, lower=priors[\"rho\"][0], upper=priors[\"rho\"][1]\n", + " )\n", + " s = pm.Beta(\"s\", 2, 2) # scaled half-width\n", + " h = pm.Deterministic(\"h\", s * (priors[\"rho\"][1] - pm.math.abs(m)))\n", + " lower = pm.Deterministic(\"lower\", m - h)\n", + " upper = pm.Deterministic(\"upper\", m + h)\n", + " rho = pm.Uniform(\"rho\", lower, upper)\n", + "\n", + " mu_treatment = pm.Deterministic(\"mu_treatment\", X_data @ beta_treatment)\n", + " mu_outcome = pm.Deterministic(\n", + " \"mu_outcome\", X_data @ beta_outcome + alpha * mu_treatment\n", + " )\n", + "\n", + " var_D = sigma_V**2\n", + " var_Y = alpha**2 * sigma_V**2 + sigma_U**2 + 2 * alpha * rho * sigma_U * sigma_V\n", + " cov_YD = alpha * sigma_V**2 + rho * sigma_U * sigma_V\n", + "\n", + " # Build 2x2 covariance matrix\n", + " cov = pm.math.stack([[var_Y, cov_YD], [cov_YD, var_D]])\n", + "\n", + " # Store as deterministic for inspection\n", + " _ = pm.Deterministic(\"var_Y\", var_Y)\n", + " _ = pm.Deterministic(\"var_D\", var_D)\n", + " _ = pm.Deterministic(\"cov_YD\", cov_YD)\n", + " _ = pm.Deterministic(\"cov_UV\", rho * sigma_U * sigma_V)\n", + "\n", + " mu = pm.math.stack([mu_outcome, mu_treatment], axis=1) # shape (n,2)\n", + " _ = pm.MvNormal(\"likelihood\", mu=mu, cov=cov, observed=observed_data)\n", + "\n", + " return dml_model\n", + "\n", + "\n", + "def make_continuous_models(data):\n", + " X = data[[col for col in data.columns if \"feature\" in col]]\n", + " Y = data[\"Y_cont\"]\n", + " T = data[\"T_cont\"]\n", + "\n", + " coords = {\n", + " \"beta_outcome\": [col for col in data.columns if \"feature\" in col],\n", + " \"beta_treatment\": [col for col in data.columns if \"feature\" in col],\n", + " \"obs\": range(data.shape[0]),\n", + " }\n", + "\n", + " spike_and_slab = make_joint_model(X, Y, T, coords, priors_type=\"spike_and_slab\")\n", + " horseshoe = make_joint_model(X, Y, T, coords, priors_type=\"horseshoe\")\n", + " excl = make_joint_model(X, Y, T, coords, priors_type=\"exclusion_restriction\")\n", + " normal = make_joint_model(X, Y, T, coords, priors_type=\"normal\")\n", + " tight_rho = make_joint_model(\n", + " X, Y, T, coords, priors_type=\"normal\", priors={\"rho\": [0.4, 0.99]}\n", + " )\n", + " tight_rho_s_s = make_joint_model(\n", + " X, Y, T, coords, priors_type=\"spike_and_slab\", priors={\"rho\": [0.4, 0.99]}\n", + " )\n", + "\n", + " models = {\n", + " \"spike_and_slab\": spike_and_slab,\n", + " \"horseshoe\": horseshoe,\n", + " \"exclusion\": excl,\n", + " \"normal\": normal,\n", + " \"tight_rho\": tight_rho,\n", + " \"tight_rho_s_s\": tight_rho_s_s,\n", + " }\n", + " return models\n", + "\n", + "\n", + "data_confounded = simulate_data(n=2500, alpha_true=3, rho=0.6)\n", + "data_unconfounded = simulate_data(n=2500, alpha_true=3, rho=0)\n", + "\n", + "models_confounded = make_continuous_models(data_confounded)\n", + "models_unconfounded = make_continuous_models(data_unconfounded)\n", + "\n", + "pm.model_to_graphviz(models_confounded[\"spike_and_slab\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This section orchestrates the fitting and sampling workflow for the suite of Bayesian models defined earlier. Having specified several variants of the joint outcome–treatment model—each differing only in its prior structure or treatment of the correlation parameter $\\rho$—we now turn to posterior inference.\n", + "\n", + "#### Various Model Specifications\n", + "\n", + "The functions `sample_model()`, and `fit_models()` provide a compact, repeatable sampling pipeline. Within the model context, it first draws from the prior predictive distribution, capturing what the model believes about the data before seeing any observations. These are comparable across each of models specified.\n", + "We're moving from describing how the data are assumed to arise, to actually learning from the simulated observations. This is the backwards inference step. The output `idata_unconfounded` contains all posterior draws, prior predictive samples, and posterior predictive simulations for every model variant under the assumption of no confounding. This will allow us to compare the inferences achieved under each setting. To gauge which are the most plausible parameterisations of the world-state conditioned on the data and our model-specification." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1ef2d789624c462db32e84221304c561", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 375 seconds.\n",
+      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
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+      "Sampling: [alpha, beta_O_raw, beta_T_raw, c2_O, c2_T, lambda_O, lambda_T, likelihood, m, rho, s, sigma_U, sigma_V, tau_O, tau_T]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [tau_O, lambda_O, c2_O, beta_O_raw, tau_T, lambda_T, c2_T, beta_T_raw, alpha, sigma_U, sigma_V, m, s, rho]\n"
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+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 150 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
+     ]
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+    },
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+     "data": {
+      "text/html": [
+       "
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+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e11dc618b70a451eafceb3d37dab7268",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 89 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c03751486e4e475091500d215f572f24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "
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+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf9a1b261711406ebeec5ce27c54f9ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 116 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
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+       "model_id": "d881928189d74af69fecc8d0065ddd64",
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+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89d5b36075d74ed9baf1401b92500f52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 86 seconds.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2487b5fedf75403bb84310b865b16d15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d5a8f079f1144ffb618824693d74010",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 392 seconds.\n",
+      "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e340bf015a5a4ec2aeadc8bfe923057b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def sample_model(model, fit_kwargs):\n",
+    "    with model:\n",
+    "        idata = pm.sample_prior_predictive()\n",
+    "        idata.extend(\n",
+    "            pm.sample(\n",
+    "                draws=1000,\n",
+    "                tune=2000,\n",
+    "                target_accept=0.95,\n",
+    "                **fit_kwargs,\n",
+    "                idata_kwargs={\"log_likelihood\": True},\n",
+    "            )\n",
+    "        )\n",
+    "        idata.extend(pm.sample_posterior_predictive(idata))\n",
+    "    return idata\n",
+    "\n",
+    "\n",
+    "fit_kwargs = {}\n",
+    "\n",
+    "\n",
+    "def fit_models(fit_kwargs, models):\n",
+    "    idata_spike_and_slab = sample_model(models[\"spike_and_slab\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_horseshoe = sample_model(models[\"horseshoe\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_excl = sample_model(models[\"exclusion\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_normal = sample_model(models[\"normal\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_normal_rho_tight = sample_model(models[\"tight_rho\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_rho_tight_s_s = sample_model(models[\"tight_rho_s_s\"], fit_kwargs=fit_kwargs)\n",
+    "\n",
+    "    idatas = {\n",
+    "        \"spike_and_slab\": idata_spike_and_slab,\n",
+    "        \"horseshoe\": idata_horseshoe,\n",
+    "        \"exclusion\": idata_excl,\n",
+    "        \"normal\": idata_normal,\n",
+    "        \"rho_tight\": idata_normal_rho_tight,\n",
+    "        \"rho_tight_spike_slab\": idata_rho_tight_s_s,\n",
+    "    }\n",
+    "\n",
+    "    return idatas\n",
+    "\n",
+    "\n",
+    "idata_unconfounded = fit_models(fit_kwargs, models_unconfounded)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before examining how different priors shape inference, it’s useful to clarify what our models are actually estimating. Each specification—spike-and-slab, horseshoe, exclusion restriction, and the others—ultimately targets the same estimand: the slope $\\alpha$ that captures how changes in the continuous treatment $T$ shift the expected outcome $Y$. In this setup, $\\alpha$ functions as a regression coefficient within the structural equation of our joint model. \n",
+    "\n",
+    "In econometric terms, what we’ve done so far sits squarely within the structural modelling tradition. We’ve written down a joint model for both the treatment and the outcome, specified their stochastic dependencies explicitly, and interpreted the slope $\\alpha$ as a structural parameter — a feature of the data-generating process itself. This parameter has a causal meaning only insofar as the model is correctly specified: if the structural form reflects how the world actually works, \n",
+    "$\\alpha$ recovers the true causal effect. By contrast, reduced-form econometrics focuses less on modelling the underlying mechanisms and more on identifying causal effects through observable associations research design — instrumental variables, difference-in-differences, or randomization. Reduced-form approaches avoid the need to specify the joint distribution of unobservables but often sacrifice interpretability: they estimate relationships that are valid for specific interventions or designs, not necessarily structural primitives.\n",
+    "\n",
+    "#### Comparing Treatment Estimates\n",
+    "\n",
+    "The comparison of models is a form of robustness checks. We want to inspect how consistent our parameter estimates are across different model specifications. Here we see how the strongly informative priors on $\\rho$ bias the treatment effect estimate.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = az.plot_forest(\n",
+    "    [\n",
+    "        idata_unconfounded[\"spike_and_slab\"],\n",
+    "        idata_unconfounded[\"horseshoe\"],\n",
+    "        idata_unconfounded[\"exclusion\"],\n",
+    "        idata_unconfounded[\"normal\"],\n",
+    "        idata_unconfounded[\"rho_tight\"],\n",
+    "        idata_unconfounded[\"rho_tight_spike_slab\"],\n",
+    "    ],\n",
+    "    var_names=[\"alpha\", \"rho\", \"beta_O\", \"beta_T\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\n",
+    "        \"spike_slab\",\n",
+    "        \"horse shoe\",\n",
+    "        \"exclusion_restriction\",\n",
+    "        \"normal\",\n",
+    "        \"tight_rho\",\n",
+    "        \"tight_rho_spike_slab\",\n",
+    "    ],\n",
+    "    figsize=(20, 15),\n",
+    ")\n",
+    "\n",
+    "ax[0].axvline(3, linestyle=\"--\", color=\"k\")\n",
+    "ax[0].set_title(\n",
+    "    \"Comparing Parameter Estimates across Model Specifications\", fontsize=15\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the plot we can see that the majority of models accurately estimate the true treatment effect $\\alpha$ except in the cases where we have explicitly placed an opinionated prior on the $\\rho$ parameter in the model. These priors pull the $\\alpha$ estimate away from the true data generating process. The variable selection priors considerably shrink the uncertainty in the treatment estimates seemingly picking out the implicit instrument structure aping the application of instrumental variables. \n",
+    "\n",
+    "Our Bayesian setup here is intentionally structural. We specify how both treatment and outcome arise from common covariates and latent confounding structures. However, the boundary between structural and reduced-form reasoning becomes fluid when we begin to treat latent variables or exclusion restrictions as data-driven “instruments.” In that sense, the structural Bayesian approach can emulate reduced-form logic within a generative model — an idea we’ll develop further when we move from unconfounded to confounded data and later when we impute potential outcomes directly. \n",
+    "\n",
+    "But for now let's continue to examine the relationships between these structural parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2, 3, figsize=(15, 8), sharex=True, sharey=True)\n",
+    "axs = axs.flatten()\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"spike_and_slab\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[0],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"horseshoe\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[1],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"exclusion\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[2],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"normal\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[3],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"rho_tight\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[4],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"rho_tight_spike_slab\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[5],\n",
+    ")\n",
+    "for ax, m in zip(\n",
+    "    axs,\n",
+    "    [\n",
+    "        \"spike_slab\",\n",
+    "        \"horse shoe\",\n",
+    "        \"exclusion_restriction\",\n",
+    "        \"normal\",\n",
+    "        \"tight_rho\",\n",
+    "        \"tight_rho_spike_slab\",\n",
+    "    ],\n",
+    "):\n",
+    "    ax.axvline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n",
+    "    ax.axhline(0, linestyle=\"--\", color=\"red\", label=\"True rho\")\n",
+    "    ax.set_title(f\"Posterior Relationship {m}\")\n",
+    "    ax.legend(loc=\"lower left\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Up to this point, we have looked at posterior summaries of individual parameters, such as the treatment effect $\\alpha$ or the correlation $\\rho$. While these marginal summaries are useful, they can obscure important interactions between parameters. In a structural model, the slope $\\alpha$ does not exist in isolation. Its interpretation depends on the joint distribution of the latent errors and the covariates that generate the treatment and outcome.\n",
+    "\n",
+    "The pairwise posterior plots below examine the joint distributions of $\\alpha$ and $\\rho$ and across different prior specifications. Each subplot shows the density of the posterior draws, highlighting how the inferred treatment effect co-varies with the estimated correlation between latent errors. The dashed vertical line marks the true causal effect, and the horizontal line shows the true values. \n",
+    "\n",
+    "By inspecting these joint distributions, we gain several insights: aggressive priors on $\\rho$ can pull the posterior of away from zero, which in turn shifts the distribution of the treatment effect estimate. But additionlly variable selection schemes like the spike-and-slab or horseshoe can significantly reduce uncertainty in the estimation of both $\\rho$ and $\\alpha$. This illustrates the trade-off between automated variable selection, prior specification. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
rho_tightalpha2.4540.2651.9502.9290.0100.007809.01456.01.01
rho0.4540.1730.1330.7740.0060.003796.01468.01.01
normalalpha3.0790.4172.3083.8660.0210.012400.0868.01.01
rho-0.0750.347-0.6900.5460.0170.007401.0858.01.01
spike_slabalpha3.0290.1292.7903.2580.0030.0031514.01345.01.00
rho-0.0400.130-0.2820.1980.0030.0021515.01393.01.00
horseshoealpha3.1670.2542.7633.7280.0160.016395.0330.01.01
rho-0.1610.219-0.6530.1690.0120.009395.0348.01.01
exclusion_restrictionalpha3.0180.1162.7973.2370.0030.0021804.01915.01.00
rho-0.0300.118-0.2600.1850.0030.0021847.01832.01.00
tight_rho_spike_slabalpha2.8290.1412.5773.0540.0070.012779.0411.01.00
rho0.1610.126-0.0720.3860.0060.007800.0426.01.00
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                              mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
+       "rho_tight             alpha  2.454  0.265   1.950    2.929      0.010   \n",
+       "                      rho    0.454  0.173   0.133    0.774      0.006   \n",
+       "normal                alpha  3.079  0.417   2.308    3.866      0.021   \n",
+       "                      rho   -0.075  0.347  -0.690    0.546      0.017   \n",
+       "spike_slab            alpha  3.029  0.129   2.790    3.258      0.003   \n",
+       "                      rho   -0.040  0.130  -0.282    0.198      0.003   \n",
+       "horseshoe             alpha  3.167  0.254   2.763    3.728      0.016   \n",
+       "                      rho   -0.161  0.219  -0.653    0.169      0.012   \n",
+       "exclusion_restriction alpha  3.018  0.116   2.797    3.237      0.003   \n",
+       "                      rho   -0.030  0.118  -0.260    0.185      0.003   \n",
+       "tight_rho_spike_slab  alpha  2.829  0.141   2.577    3.054      0.007   \n",
+       "                      rho    0.161  0.126  -0.072    0.386      0.006   \n",
+       "\n",
+       "                             mcse_sd  ess_bulk  ess_tail  r_hat  \n",
+       "rho_tight             alpha    0.007     809.0    1456.0   1.01  \n",
+       "                      rho      0.003     796.0    1468.0   1.01  \n",
+       "normal                alpha    0.012     400.0     868.0   1.01  \n",
+       "                      rho      0.007     401.0     858.0   1.01  \n",
+       "spike_slab            alpha    0.003    1514.0    1345.0   1.00  \n",
+       "                      rho      0.002    1515.0    1393.0   1.00  \n",
+       "horseshoe             alpha    0.016     395.0     330.0   1.01  \n",
+       "                      rho      0.009     395.0     348.0   1.01  \n",
+       "exclusion_restriction alpha    0.002    1804.0    1915.0   1.00  \n",
+       "                      rho      0.002    1847.0    1832.0   1.00  \n",
+       "tight_rho_spike_slab  alpha    0.012     779.0     411.0   1.00  \n",
+       "                      rho      0.007     800.0     426.0   1.00  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_params = pd.concat(\n",
+    "    {\n",
+    "        \"rho_tight\": az.summary(\n",
+    "            idata_unconfounded[\"rho_tight\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"normal\": az.summary(idata_unconfounded[\"normal\"], var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"spike_slab\": az.summary(\n",
+    "            idata_unconfounded[\"spike_and_slab\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"horseshoe\": az.summary(\n",
+    "            idata_unconfounded[\"horseshoe\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"exclusion_restriction\": az.summary(\n",
+    "            idata_unconfounded[\"exclusion\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"tight_rho_spike_slab\": az.summary(\n",
+    "            idata_unconfounded[\"rho_tight_spike_slab\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "df_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly we can compare the models on holistic performance measures like leave-one-out cross validation. Note however, that the primary purpose here is to showcase sensitivity of the parameter of interest to model specifications. We're not necessarily seeking to enshrine one model as the best. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_df = az.compare(idata_unconfounded)\n",
+    "az.plot_compare(compare_df, figsize=(15, 7));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The tables highlights the model's sensitivity to priors. Sparse priors, like spike-and-slab and horseshoe, can slightly shrink coefficients and influence the posterior spread, particularly for $\\rho$, but strong priors directly on $\\rho$ can negatively impact the estimation routine. especially when there is no true correlation. This is not a flaw. It is a feature. In practical settings, treatments and outcomes are often correlated due to unobserved confounding, measurement error, or endogenous selection. For example, in a health economics study, patients who choose a particular therapy may do so because of unobserved health determinents that also influence recovery—such as risk tolerance, underlying severity, or access to informal support. In labor economics, higher wages may appear to cause greater job satisfaction, but workers who are more motivated or more socially connected might self-select into higher-paying jobs, creating correlation between the unobserved determinants of treatment and outcome By exposing the model to different prior assumptions, we can probe how strong beliefs about sparsity or instrument validity propagate into causal estimates.\n",
+    "\n",
+    "In other words, prior sensitivity is a diagnostic tool as much as a regularization mechanism. When $\\rho$ is expected to be nonzero i.e. in observational studies with likely latent confounding, then explicitly modelling its distribution becomes crucial. The unconfounded case, therefore, serves as a baseline: it confirms that our joint Bayesian model can recover true parameters when the world is simple, while setting the stage for exploring more realistic, confounded scenarios where these structural dependencies must be handled carefully."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Confounded Case\n",
+    "\n",
+    "While the unconfounded case provides a useful benchmark, most real-world observational studies involve some degree of endogenous treatment assignment. In our simulations, this occurs when the residuals of the treatment and outcome equations are correlated. This demonstrates that controlling only for measured variables is insufficient when unobserved confounders influence both treatment and outcome.\n",
+    "\n",
+    "The Bayesian joint model provides a principled solution. By explicitly modelling the correlation between treatment and outcome residuals, the framework can adjust for latent confounding while still estimating the causal slope $\\alpha$. Moreover, flexible priors such as spike-and-slab and horseshoe allow the model to automatically discover potential instruments i.e. covariates that predict the treatment but not the outcome. The theory is that the instrument structure if it holds in the world is also the one which best calibrates our parameters. These instruments help disentangle the structural effect of the treatment from latent correlations, improving identification.\n",
+    "\n",
+    "By setting $\\rho$ = 0.6 we simulate a moderate level of confounding—similar in spirit to cases where unmeasured preferences, abilities, or environmental factors drive both exposure and response. Conceptually, this setup mimics situations such as:\n",
+    "\n",
+    "-  More health-conscious individuals being both more likely to adopt a preventive therapy and more likely to recover quickly.\n",
+    "\n",
+    "- High-income households being more likely to invest in cleaner technologies and experience better environmental outcomes.\n",
+    "\n",
+    "- Firms with stronger internal capabilities both adopting new management practices and achieving higher productivity.\n",
+    "\n",
+    "Under such conditions, simple regression cannot disentangle correlation from causation, as the treatment is no longer independent of the unobserved outcome drivers.\n"
+   ]
+  },
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+      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
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+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 484 seconds.\n",
+      "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
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+      "Sampling: [alpha, beta_O_raw, beta_T_raw, c2_O, c2_T, lambda_O, lambda_T, likelihood, m, rho, s, sigma_U, sigma_V, tau_O, tau_T]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [tau_O, lambda_O, c2_O, beta_O_raw, tau_T, lambda_T, c2_T, beta_T_raw, alpha, sigma_U, sigma_V, m, s, rho]\n"
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+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 259 seconds.\n",
+      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
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+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
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+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 142 seconds.\n",
+      "There were 2 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
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+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
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+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 148 seconds.\n",
+      "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
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+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
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+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 96 seconds.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
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+       "Output()"
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+     "output_type": "display_data"
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
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+       "model_id": "f0f8a3f8c85641eea974ff23ebec0cc6",
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+    },
+    {
+     "data": {
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+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 454 seconds.\n",
+      "There were 11 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      "text/plain": [
+       "Output()"
+      ]
+     },
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+    {
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+      "text/plain": []
+     },
+     "metadata": {},
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+   ],
+   "source": [
+    "idata_confounded = fit_models(fit_kwargs, models_confounded)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can again compare these models on predictive performance measures, but the real focus is on the success of the causal identification within these model specifications. The performance metrics also highlight that they're broadly similar models. Another way to see this is that grading models on predictive performance does not ensure that the model correctly identifies the causal mechanism. Indistinguishable, predictive performance does not ensure mechanistic accuracy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_df = az.compare(idata_confounded)\n",
+    "az.plot_compare(compare_df, figsize=(15, 8));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Comparing Treatment Estimates\n",
+    "\n",
+    "The forest plot below compares posterior estimates of the treatment effect ($\\alpha$) and the confounding correlation ($\\rho$) across model specifications when \n",
+    "$\\rho = .6$ in the data-generating process. The baseline normal model (which places diffuse priors on all parameters) clearly reflects the presence of endogeneity. Its posterior mean for $\\alpha$ is biased upward relative to the true value of 3, and the estimated $\\rho$ is positive, confirming that the model detects correlation between treatment and outcome disturbances. This behaviour mirrors the familiar bias of OLS under confounding: without structural constraints or informative priors, the model attributes part of the outcome variation caused by unobserved factors to the treatment itself. This inflates and corrupts our treatment effect estimate. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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10qnvn3/+EWq1WtjY2Oi1tWbNGgFA1KpVy2AMaR3rkydPCrlcLtzc3HSOmUajEdOmTRMARKdOnXS2ycy5lJasJNx8fX2FTCYTZ8+e1Vt3+vRpERkZqX2dkjgzlHQWIuOEGwAxcuRI7bWl0WhEYGCg9r2qVKmSuH//vna7lMSKoXNz8+bN4u3btzrLIiMjRevWrQUAsWbNGoMxpBW7se9/TEyMUCqVwt3dXbx48UKnfEJCgl4d6dm5c6fOOSqEELGxseLjjz8WAERwcLDOuhs3bgiVSiUUCoXYsGGDzrqkpCSxfft2nWUZXX8px6x3794iPj5eu/zo0aNCrVYLExMTnfM35byfP3++Xl13794Vt27d0r5OOZ83bdqkV/bq1at6Cee0pE64JSUlieLFi4tSpUpp10dHRwsrKytRr149IYRIM+Fm7L5GRkZqExi//vqrdnlCQoLOtfpuOy1atBAAxNixY3XauX37tvD09NRrRwjjEm5CCJ37fc2aNcWUKVPE7t27003Mp75vfvHFFzr36m+//VYAEMWKFRMxMTHa5Vm5N/78888CgChRooTefr59+1YcOHAgU/tu6HMzhaGEW0JCgjaxNmrUKJ1jL4QQ586d07nHZOfzLC2GEm4ajUb7BYMJEybo1Pn7778LuVwurKysxOPHj7XLs/L3gTHXJhEREWUfE25EREREZJSUB1eGegOl5+3bt8Lc3FzI5XJx48YNvfWLFy8WAERAQIDO8pQHWe8mzoQQokOHDgYTJUIIsXXrVu03zlNLefBVv359vW3i4+NFyZIlBQBx4sSJTO3X8uXLBQCxePFig+20atUqzW3TS7jJ5XJx/fp1vW0+/PBDvQd3KTG8e+yEEOL169fa3oDZSbjFx8eLS5cuiVatWml7MGTUu0oIoe3tcenSJZ3lKfs+b968TMWU2v79+7WJmtRSHiwaOh+2bdum7Vlx8+ZNnXWJiYnCwcFBKBQKnYexw4YNEwDE999/bzCOdu3aCQAiNDRUL4a0jnXbtm0FAIO9/jQajahevbqQy+U6D1Izcy6lJSXhltHPq1evtNuUL19e2NraZqr+7CbcPD09RUJCgs66S5cuaeM6ePCgXp0pvU3eTXKn5ebNmwKA6NChg1GxG/v+P3nyRAAQbdu2zVRcWREdHS1MTU31vhTw6aefCgBiyJAhmaonvevv9u3bAoCwtrbWOS9SpPRSGjRokF7758+fz7DtlMSTobqNkTrhJoQQo0eP1rl3//rrrwKAWLp0qRDCcMItK/u6YsUKAUA0a9ZMr/zLly+FpaWlXjvnz5/Xfu68m6wRIrnHHPBfr+EUxibcoqKiRO/evYVMJtO5vk1MTISfn5/B+07KcXRzc9O7FoVI7m35bnIxK/fGlB6MGzduzNS+5FTCbcOGDQJI7t317pdmjJXR51laDCXcDh48KIDkXs2pe9+mSPn7JnXPx6z8fWDMtUlERETZZwoiIiIiIiOINOYPy0hoaChiYmJQt25deHl56a3v3bs3hg4diuPHj0MIoTe3WbNmzfS2KVWqVJrrPD09ASDN+YC6deumt0yhUKBjx45YtGgRjh07hnr16umsP3bsGEJCQvDvv/8iNjYWQght/Tdv3jTYTps2bQwuz4i7uzvKlCmjtzxlWer9OnHiBACgc+fOeuWtra3h7++PTZs2GR3DvXv39N4HAHBycsKmTZtgY2OjXRYXF4c9e/bgr7/+wrNnzxAfHw8geY4fIPn4VK5cWa+ujI7PhQsXsG/fPty7dw/R0dEQQuDNmzfaOg1J71xxd3dH6dKlddaZmJjA3d0dZ8+exfPnz+Hi4gIA2L9/PwCgbdu2Bttp0KABtmzZgjNnzqBGjRrp7gcAaDQaHDhwAFZWVmjSpIneeplMBh8fH5w/fx6hoaEICAjQWZ/VcwlIfs+aN2+e5nozMzPt797e3li7di0GDBiAESNG6M3DmJP8/Pxgaqr739KU98rMzAy+vr5623h6euL8+fN49OiRzjxJQPI5sWvXLty6dQtRUVHQaDTae1Za50tajH3/HR0dUaJECezcuRPz5s1Dz549UaxYMaPaTO3ff//F9u3bce3aNURGRmrnLTQzM9Pblz///BMAMGjQIKPaMHROHTt2DADQsmVL2Nra6q3v3bs3FixYgKNHj2qXeXt7AwAGDx6ML7/8Eg0bNtR7X1OX3b17N/r06YNJkyahZs2akMuzP718r1698NVXX2Ht2rWoV68e1q5dC4VCga5du6a5TVb2NWWbLl266JW3s7ODv78//vjjD53lqc8lQ/fUBg0aAECac15mllqtxk8//YSJEydi48aNOHbsGM6cOYMXL14gJCQEISEhmD9/PkaOHKm3bceOHQ2+Z927d8fp06dx7NgxdO/eXW9/DHn32nj48CHCwsK0c4vmpZRrY+DAgZk+z7L6eWaMlHOqa9euMDEx0Vvfu3dv/PHHHzh69CjGjx+vs86Yvw+MuTaJiIgo+/gpS0RERERGcXBwwPXr1/Hs2TOjtnv48CEA6D0gT2FrawsbGxu8fv0akZGROskcAChevLjeNhYWFhmui4uLM9iem5ubweUp8aXECwCvX79Ghw4dcPDgQYPbANAmgd7l6uqa5jbpKVGihMHllpaWAHT3KyXWkiVL5mgMFhYW6NSpE4DkpJStrS1q1KiB9u3bQ61Wa8tdvnwZbdq0wd27d9Osy9jjEx8fj8DAQKxbt87oOo09V1KvT31cU/bH2dk5zRgA4Pnz5+muT/HixQu8ffsWADJ84Gmozqy+jwBQrlw5rF69OlNlZ86cicuXL2PlypVYuXIlHBwcUL9+fbRr1w49evSAUqnMchzvSu+9cnZ2NviA3NB7JYTA6NGjsXDhwjS/FJDW+ZKWrLz/a9asQbdu3TB27FiMHTsWHh4eaNSoEXr06AF/f/9Mt71gwQJMmDBB+6A/I/fv3wfwX7IyswydUxndqw3dIwMDA7Fv3z789ttvaNy4MdRqNWrWrIkWLVqgf//+cHR01JYdM2YMjh07hu3bt2P79u2wsbFBnTp10Lp1awQGBsLKysqofUhRtWpVVK5cGb/99hsmTpyIffv2oUWLFihSpEia22RlX1N+T+t6NLQ85VwaN24cxo0bl2Y8mb2XZKRs2bKYOHEigORE/8mTJzFhwgQcPXoU48aNQ8eOHfU+A435TDT22kg5P1O+CJOXjG07O59nxsjKuZfCmL8PjLk2iYiIKPuYcCMiIiIio1SrVg3Hjx/HuXPn0KtXL6O3N/Tt/syUSW+7zNSZWYYe1o8bNw4HDx5Eo0aNMG3aNFSqVAm2trYwMTHBvn37EBAQkOZDfpVKlaU4srJPaW2T1V6JDg4OGSZphBDo0qUL7t69i08++QSffPIJSpUqBUtLS8hkMnzxxReYNWuW0cdnwYIFWLduHSpVqoR58+ahRo0asLOzg0KhwI0bN1C2bNk068ypcyUpKQkymQx9+vRJt1zFihUzXR8AWFlZoUOHDumWNfTwO6vnkrFKliyJs2fP4uDBg9ixYwcOHz6M7du3Y9u2bZg7dy5OnDgBOzu7HGkrp96rDRs2YMGCBShRogQWLVqEevXqoWjRolAoFIiPj4dSqTT6OsjK+9+4cWPcunULO3bswJ49e3D48GGsWbMGa9asQZcuXbBhw4YM2z116hRGjRoFGxsb/PDDD/Dz84Ozs7M20VmsWDGDPXdlMpnR9430zqm06kpZnnq9iYkJNmzYgPHjx2Pr1q04dOgQTp06hSNHjmDWrFnYu3cv6tatCyC51+3Bgwdx/PhxbN++HSEhIThw4AD27duHWbNm4ejRo1lOzPTs2RPjx4/HgAEDkJiYmOnPKGP2NeU8MvZeAgANGzZMNynq4OCQ6TozSy6Xw8fHB3v27EG5cuVw//597N27Fx9//HGmtjd03WT13piTn9XGykzb2f08y424jP176F3GXJtERESUfUy4EREREZFRWrVqhaVLl+L333/H3LlzMz00UcrQanfu3DG4/vXr13j9+jUsLCyy3MPBGPfu3TO4PDw8HAB0hoLbvHkzTExMsG3bNr2ed//880/uBZlJKUMgpsT+rpRv+OeGa9eu4dq1a6hZsyaWLVumtz6rx2fz5s0AoE265USdxipRogRu376NxYsXw9raOtv1OTg4QKlUQqFQZLq3mVRMTU3h7++v7ZUVHh6Ofv364eDBg5g9ezbmzJkjcYS6Us6XZcuWoXXr1jrrsnq+ZPX9t7a2Ro8ePdCjRw8AyQm0zp0747fffkNgYCBatGiR7vYp+/Lll1+ib9++OutiYmLw+PFjvW1KliyJmzdv4vbt29keAjSje3VKz5+U+05q1atXR/Xq1REUFITIyEgEBwdjwYIFGDZsGE6fPq0tJ5PJ0KBBA+1Qis+ePcOwYcOwbt06fPHFF5lKTBrSs2dPTJgwAXv27IG1tXWGw7BmZV9TtsnoMyS1lB5JnTp1wtChQ9PfiVyiVqtRu3Zt3L9/32BPOmM+E429NlJ6X9+6dSsroWeLMW3n1ueZIRmdeynvh6HrLCsye20SERFR9mR/oHQiIiIieq80b94cFStWxIMHDzBjxox0y0ZGRuLKlSsAkucRMTc3x19//WVwLqW1a9cCSJ77JS++BW/ogW5iYqJ2rjMfHx/t8levXsHKykov2QYAv/32W+4FmUn169cHAGzcuFFvXWRkpHa+ndzw6tUrAIaHuHr16lWW206p19AwmXl1zJs2bQoA2LJlS6a3SZkLLTExUW+dqakp/Pz88PLlSxw5ciRHYswrrq6u2qHwUuYxAtLf37yUlfMlo9iz8v4bUrduXfTu3RuA7rFLS3r78vvvvxvsXZMS6w8//JCdUAH8N5/Yzp07ERERobc+5V7dsGHDdOuxtrbGzJkzIZPJMtzvokWLIigoCEDmjlFaSpQogVatWqFIkSLo1atXhr1Cs7KvKdv8/vvveuUjIiKwb98+veU5dS5l1+3btwHA4NyCmzZt0vbES239+vUAdD8Tjd2fYsWKoXz58njx4oXe/Ha5LSXWH3/8McOeaVn9PMvKfTDlnNqwYYPB457Z6ywrjLk2iYiIyDhMuBERERGRUWQyGdauXQuVSoWgoCBMmDABUVFROmWEENi2bRtq1qyJM2fOAEied6l///7QaDQYPHiwzjY3btzAl19+CQD4/PPP82Q/jh8/jpUrV+rEPHXqVISHh6Nq1araJBYAlClTBhEREXpJuoULF+LQoUN5Em96OnfuDDs7O+zZs0ebMASS5+4ZN24cIiMjc63t0qVLQy6X4+DBgzqJ1NjYWHzyySd4+fJlluotU6YMAOC7777TWb5x40b89NNPWQ/YCKNGjYK5uTlGjBiB7du3661/+fIlvv32W8TExGiXpTzIvn79usE6v/jiC8jlcvTt2xfHjh3TW//w4UMsXbo0h/YgaxYuXIgnT57oLd+zZw8A3TmqMtrfvJJyvvzwww86D9WPHj2KefPmGdwmo9iNff/Dw8OxevVqREdH65SLi4vT3icyMw9fyr6sWLECCQkJ2uVXr15Nc/6v4cOHQ6VS4bvvvtO5BwDJ94Fdu3Zl2G6KUqVKoVWrVnjz5g2GDRumE8PJkyexbNkymJiY4LPPPtMu//nnn/H333/r1bVnzx4IIXT2+7vvvjPYq2f37t0AsjdXIQBs374dz58/z9R1lJV97dy5M+zt7bXzYqVISkrCqFGjtPM0pla3bl00adIEhw4dwogRI/TKaDQa7Nu3z+A9IbMiIiJQp04dbN68WWc/ACAhIQFffvklLly4AHNzczRv3lxv+3v37iE4OFhn2Q8//ICTJ0/C2dkZ7du31y7Pyr1x/PjxAJLP1ZQv4qSIiopKd47U7OjQoQPKlCmDixcvYvz48XpJsQsXLuDBgwcAsv55lpX7oJ+fHypXrow7d+5gypQpOvetLVu24I8//oClpSUCAwMzXachxlybRERElAMEEREREVEWHDt2TDg5OQkAQq1WiyZNmogePXqIVq1aaZerVCrx559/areJjIwU3t7eAoBwdHQUnTt3Fi1bthQqlUoAEEOHDtVrx83NTaT1Z+vUqVMFALFq1Sq9dXfu3BEAhK+vr87yvn37CgDi008/FTKZTNSuXVt0795dVKxYUQAQVlZW4ty5czrbrF27VgAQAETDhg1F9+7dRYUKFYRcLhcjRowQAETfvn0NtnPo0KE0j6GhfUsr7oz2ecOGDUIulwsAokGDBqJ79+7Cy8tL2NjYiF69egkA4pdffkkzFkMxuLm5Zar8wIEDBQBhbm4uWrVqJTp16iScnJyEg4ODCAwMNBhveu+rEEIcPnxYmJiYCADC29tbdO/eXdSsWVMAEKNHjzZ4jLJyPqTw9fUVAMSdO3d0lm/atEmYm5sLAKJs2bKiXbt2om3btqJatWrCzMxMABCvXr3Slv/333+FSqUSJiYmonnz5qJ///5iwIAB4tq1a9oyS5Ys0e5blSpVRMeOHUWrVq1EpUqVhImJibCxsdGJITPnUloOHTokAAgnJyfRt2/fNH9Snxs2NjZCLpeL6tWriy5duojOnTuLsmXLCgDCwcFB3Lp1S1s2JiZGODo6ao9tv379xIABA8Tx48fTPe6rVq0SAMTUqVMNxp3e+WfoeFy/fl1YWFgIAKJChQqiW7duomHDhkImk2nPl3fryyh2IYx7/8+fP6+9HzZq1Ej06NFDtG3bVhQtWlQAELVr1xZxcXHpv2FCiOfPnwtnZ2cBQHh4eIguXbqIpk2bCoVCITp37pzmtfPrr78KhUIhAIhKlSqJbt26iYCAAFGsWDG98hldfw8ePBAeHh7a49atWzfRpEkT7Xk7f/58nfJt27YVAISnp6do166d6N69u6hXr56QyWTCxMREbNq0SVu2atWq2vepY8eOomvXrqJatWrae8iJEycyPEZC/He9z5o1K1PlBw0aZPD+YOy+CiHExo0btfdbHx8f0b17d+Hp6Smsra1Fz549Dbbz+PFjUaVKFQFA2Nvbi8aNG4uuXbuKBg0aaM+RhQsX6mxjzH341atX2s8pa2tr8cEHH4ju3buLFi1aCBcXFwFAmJiYiNWrV+tsl3IcBw4cKBQKhahYsaLo3r27qFWrlgAgFAqF2L17t157xt4bhRBiyJAh2jhSPkt9fX2FtbW13j0irX1P79xN6z5++fJl7TVVrFgx0alTJ9GuXTtRvnx5vXtJVj7P5s+fr73PduvWTQwYMECMGzdOuz6te/ilS5dEkSJFBABRvnx50b17d+Hj4yMACFNTU/Hbb7/plM/K3wfGXJtERESUfUy4EREREVGWvXnzRnz11VfC19dXFC1aVJiamgpbW1tRp04dMXXqVHH//n29bd6+fSuCg4NFhQoVhFKpFFZWVqJBgwbi119/NdhGbiXcDh06JLZv3y7q1asn1Gq1sLGxEW3bthVXrlwx2NbOnTtF3bp1hZWVlbC1tRVNmzYVISEh2oSG1Ak3IYTYt2+faNCggTA3Nxe2traibdu24tq1a+Kjjz4SAMSePXvSjMVQDJl90JuYmCjmz58vKlSoIFQqlXBychI9e/YUd+/eTTPejB74CyHEyZMnRePGjYWdnZ2wsrIS9evXF5s2bUrzGOVGwk0IIW7cuCEGDRokSpUqJZRKpbCxsRHly5cX/fr1Ezt27BAajUan/N69e4WPj4+wtLTUPgB/9zw4e/as6NmzpyhZsqRQKBTC3t5eVKlSRQwePFiEhITolM2JhFtGP8OGDdNu89NPP4kePXqIsmXLCisrK2FlZSUqVKggRo8eLR4+fKjXxpkzZ0SzZs2EjY2NkMlkOu9BXiXchBDi6tWr4sMPPxSOjo5CrVaL6tWrix9++CHd+tKLPUVm3//IyEjx1VdfiZYtWwp3d3ehUqmEg4ODqFWrlli8eLGIjo42uD+G3L9/X/To0UMUL15cqFQqUb58eTFr1iyRmJiY7rVz4cIF0aNHD+Hi4iIUCoVwcnISvr6+YvHixTrlMnP9PX/+XIwaNUp4enoKMzMzYWtrK/z9/cXevXv1yh4+fFgMHjxYVKtWTRQpUkSoVCrh6ekpevToofcFhm3bton+/fuLihUrCltbW6FWq0WZMmXExx9/LG7evJnpY5RTCTdj9zXFgQMHRMOGDYVarRa2trbiww8/FFeuXEn3PhQdHS0WLFgg6tSpI6ysrIRSqRTu7u7C399fLF26VDx79kynvDH3YY1GI06cOCGmTJkiGjVqJNzc3ISZmZkwNzcXZcuWFR999JG4ePGi3nap4z1x4oRo0qSJsLKyEpaWlqJJkyY6Ceh3GXtvFEKIzZs3C39/f2FnZyfMzMyEq6uraN++vdi1a1em9j0rCTchkhOeo0aNEl5eXkKpVAo7OztRrVo1MWnSJPHixQttuax8niUkJIhJkyYJT09PbdI7dezp3cPv3bsnBg4cqP0scHBwEO3atROnT5/WK5uVvw+MuTaJiIgo+2RCZDCINRERERFRIRIYGIg1a9bg0KFD8PPzkzqcXKfRaFClShVcuXIFjx49grOzs9QhERFRPhEUFITg4GCsWrUq28MXEhEREb3vOIcbEREREVEh8O+//+Lp06c6yxISEjBhwgRcuXIFjRs3ZrKNiIiIiIiIKJeYSh0AERERERFl39GjR9GrVy/UqFEDbm5uiIqKwsWLF/Hw4UPY29tjyZIlUodIREREREREVGixhxsRERERUSHg7e2N3r174+XLl9izZw8OHDgAMzMzfPzxxwgNDUWFChWkDpGIiIiIiIio0OIcbkRERERERERERERERETZwB5uRERERERERERERERERNnAhBsRERERERERERERERFRNphKHQBRfiGEwJs3b6QOg4iIiIiIiIiIiIiI8hErKyvIZLJ0yzDhRvR/b968gY2NjdRhEBERERERERERERFRPvL69WtYW1unW0YmhBB5FA9RvsYebkREREREREREhkVFRaFYsWIAgIcPH8LCwkLiiIiIiPIOe7gRGUEmk2WYoSYiIiIiIiIieh+ZmJhof7e2tmbCjYiI6B1yqQMgIiIiIiIiIiIiIiIiKsiYcCMiIiIiIiIiIiIiIiLKBibciIiIiIiIiIiIiIiIiLKBc7gRERERERERERFRukxMTNCpUyft70RUeCUlJSEhIUHqMIjyjEKhyJHPNpkQQuRAPEREREREREREREREVEAJIfD48WNERERIHQpRnrO1tYWzszNkMlmW62APNyIiIiIiIiIiIiKi91xKss3R0RFqtTpbiQeigkIIgejoaDx9+hQA4OLikuW6mHAjSiUpKQkajUbqMIiIiIiIiIiIiIjyTFJSEl69eoWiRYvCzs5O6nCI8pRSqYRGo8GzZ89gZ2enN7ykQqHIVD1MuBGlcv/+fcTHx0sdBhERERERERFRvhIdHY3q1asDAM6fPw+1Wi1xRESUkzQaDRITE6FQKPh8lN5LCoUCiYmJuHv3LuRyuc66MmXKZKoOJtyIUomPj4eJiQlMTXlpEBERERERERGlSEpK0v6uVCqhVColjIaIclpiYiISEhIgl8s5lCS9l1LOfYVCoZMfSExMzHQdzCoQvcPU1DTTXUSJiIiIiIiIiN4HqZ+VKBQKPjshKmRkMpk20caEG72vDCXcjCHPuAgREREREREREREREVH+d/jwYSiVSkRERGR6m+nTp6NWrVq5F1QqzZo1w6hRo/Kkrax491h89NFH6NSpU57H8dNPP8HR0dGobcqUKYPFixfnUkQZY8KNiIiIiIiIiIiIiIgKpHcTWPXq1cO9e/dgY2OTq+0UViNGjMCePXukDqNA4pCSRERERERERERERIXMnRcxePA6HiVszOBRxFzqcIjyjJmZGZydnaUOQ48QQmc+zPzK0tISlpaWUodRIDHhRkREREREREREVIi8ik7I8TqjU9X5KjoBccj5NihnvI5NxLxDD3D+37faZdWLW2LMByVgo+Lj4MLKTv1+zqv40Ucf4ciRIzhy5Ai++eYbAMDy5csxcOBAPHnyBLa2tgCAFStWYObMmXjx4gWaNWsGHx8fzJw5E0+fPtWp75dffkFwcDBevXqFgIAALFu2DFZWVgbbuX79Otzd3dOM7fDhw/D398f27dsxdepUXL58GTt27AAAaDQaTJgwAatWrYKZmRkGDhyIyZMna7cNDw/HiBEjcOjQIcjlcvj7+2PhwoVwcnLK8JhcunQJo0ePRmhoKGQyGUqXLo2lS5fC29sbP/30E0aPHo3ly5fjiy++wP379+Hj44MffvgBJUuWBJA8pOS2bdtw5swZg/WfO3cObdq0wZAhQzB+/Hi8fv0a48ePx/bt2xEbGwtvb2/MmzcPVapUyVas77p9+zbGjh2Lv/76C1FRUShXrhymT5+OJk2a6JR7+/Yt+vTpgx07dsDa2hpjxozB4MGDM4wlJ/AOS0REREREREREuSImIf9/k78wavXj3zlep0iMh3mpmgCATj9dg8zULMfboJwhlwFWKgWW9qiBWh52OHPnFb7YfBm9frkGjZA6OsotBz7NOLmRnqTEJGiEgAC054kQAonxcdkPzoCM5royVSohk8kyrGf+/Pm4efMmKlSogKlTpwIArl69qlPmxIkTGDJkCGbMmIHWrVvj4MGDCA4O1qvrn3/+wbZt27B582a8evUKPXv2xLx58zBt2jSD7RQtWjRT+/rFF19g9uzZ8PDw0CYA165di2HDhuHo0aM4ffo0PvroI9SrVw9NmzaFEAKdO3eGhYUF/vzzTyQmJmLo0KHo1asX9u/fn2F7ffv2RbVq1bB48WKYmJjg0qVLUCj+S8hGR0djzpw5WLFiBczMzDB06FD07t0bISEhGdZ9+PBhdO7cGdOnT8egQYMghEC7du1gZ2eHrVu3wtraGj/++COaN2+Ov//+G/b29tmKNbWoqCg0b94cwcHBUCqVWLt2LTp06IDLly/D1dVVW27BggUYN24cJk2ahP3792PMmDEoW7YsmjZtmuH+ZRcTbkRERERERERElCuaLLskdQiUQ2SmZnDsHCR1GJQJGgHMbF8Zraq4AABaVXGBgMCQX89LHBnlpuzeb4tbmSDoA0eIl7GQmWoAAElxsTj4xUc5EZ7RPln+KxQqVYblbGxsYGZmBrVarR1G8vr16zplvv32WwQEBGDkyJEAgDJlyuDUqVPYtWuXTjmNRoMff/wRVlZWAIAePXrg0KFDabaTWVOmTNFL9lSuXBmTJk0CAHh5eWHZsmU4dOgQmjZtigMHDuDy5cu4fv26ttfZqlWrUK1aNZw9exY1a9ZMt7379+9j5MiRKFeunLb+1BISErBo0SLUrl0bAPDjjz+iatWqOHPmDGrVqpVmvdu2bUP//v2xdOlSdO3aFQAQEhKCv//+Gw8ePIBSqQQAzJkzB9u2bcMff/yBjz5K//zJKNbUqlSpotNrLjg4GFu3bsWOHTvw2WefaZfXq1cPY8aMAZD8Xp88eRKLFy/Ok4RbRolkIiIiIiIiIiIiIipAannY6byu7ZF+LxOiwuzGjRt6iSRDSSs3Nzdtsg0AnJ2d9YaczApDwyNWqlRJ57WzszOePXsGALh27RpKlCihTbYBQPny5WFra4tr165l2N6wYcPwySefoHnz5pg3bx5u376ts97U1FQnpnLlymVY95kzZ9CtWzesWLFCm2wDkoeXfPv2LVxcXGBvb6/9uXv3Lv75559sx5paVFQUJkyYgKpVq8LR0RH29va4fv067t+/r1Oubt26Oq/r1KmTqeOWE9jDjYiIiIiIiIiIckV2hzijrGHPQjpz55W2hxsA/HXnpYTRUF7I/pCSiYiNfgs3exWUyuSeZUKYo/TyX3MiPD2ZGVIypwgh9IanFEJ/fNV3hzKUyWQGyxnLwsIiU21pNBptbIaG00xr+bsmT56Mrl27Yvfu3di7dy+mTZuGtWvXom3btjrtvSu9ukuVKgV7e3usXr0aLVq0gJmZmTYmFxcX7Nu3T2+blOEzsxtrivHjx2P//v2YM2cOPD09oVKp0L17d8THx2fYTmaOW05gwo2IiIiIiIiIiHKFucJE6hDeSzs/qpRxISNFR0fDt25yj4jDp0KhVqtzvA3KGZN238XkrZchIFDbwx5/3XmJKVv/RvXilviyhbvU4VEuye79NlEmEC+TQYbkeQABADIZTDIxrKPUFAoFkpLSnjO0bNmyOHPmjM6yc+fO5Xg7OaV8+fK4f/8+7t+/r+3lFhYWhtevX2uHXsxImTJlUKZMGQwbNgy9e/fGmjVrtEmsxMREhIaGanv9Xb9+HREREShbtmya9RUpUgQbNmxAs2bN0LNnT/z6669QKBSoVq0aHj9+DFNTU7i7u2dpf9OLNbXjx4+jT58+2nVv377FvXv39MqdPn1a5/Vff/2V7r7lJCbciIiIiIiIiIiIChE7tSLjQkZSQoGYmBht/epcaINyxoyWHgjae1dnzrbarlYICnCHrTkfB1Ph4+bmhjNnzuDu3buwtLTU9hRL8dlnn6FJkyZYtGgRWrVqhZCQEOzdu9foXk/vtmNvbw+5POdn7WrSpAkqV66MwMBAfPXVV0hMTMTQoUPRqFEjg8NTphYTE4Px48ejQ4cOcHd3x7///ouzZ8+iffv22jIKhQIjRozAggULoFAoMHz4cNSpUyfd+dsAwNHREXv37oW/vz969+6NtWvXokmTJqhbty46d+6MGTNmoEyZMnj06BH27NmDNm3apBtvZmJNzdPTE1u2bEGrVq0gk8kQFBSk914DwMmTJ/HVV1+hTZs2OHDgADZt2oQtW7aku285hXO4ERERERERERERERUStuamWNSuNH7pWQ5zWpfCLz3LYVG70rmebDN9dQvKuwdh+upWrrZD9K4RI0bAxMQE1apVQ/HixfXm9Kpfvz6++eYbLF68GLVq1cK+ffswdOhQqIzsvfduO+Hh4Tm5G1oymQy///47bG1t0aRJE7Ro0QIeHh5Yu3ZthtuamJjg5cuXGDBgACpVqoSePXsiICAAU6ZM0ZZRq9UYPXo0+vTpg0aNGkGlUuHnn3/OVGzOzs7Yu3cv/v77b/Tt2xcajQZbt25FgwYNMGjQIFSqVAm9e/fGvXv34OjomO1YU5s3bx7s7Ozg6+uLDh06oFmzZqhevbpeueHDh+P8+fOoU6cOZs2ahTlz5sDf3z9T+5ddMpETg5ASFRI3btyAUqnUG0OXiIiIiIiIiOh9Fh0dDS8vLwDAzZs3c21ISXkM5xoraGSxEbA9FgTlo/+G7ItzqYWIBkEQKlvpAiuANOb2krWdmJiIyMhIuLq6Gp2IKog+/fRTXL9+HQcPHpQ6lDz1008/YfTo0Xj69KnUoeQ7sbGxCA8Ph7W1NUxN//uCQkJCAtzc3DJVB/sQExERERERERFRoSZLiJY6hAIv9TGUJURDlpA77Tj/7JM7FVPukckBpTXQeTXgWh8IPwHl9uFw2vghIPSHe6O0PeoXKl3jiUn/f79E3r1vsrwbgG/BggVo2rQp1Go19u7di59//hmLFy/Os/bp/cCEGxERERERERERFWouq9Kf84YyFhX/3yBZzj83gIWZcXMfUSEmNMCHi4CK/593qWJ7QAhgYz9JwyqIpLxXxVqWRJTPfJhFJMLMNG+u73iHinnSDgCcPXsWCxYswJs3b+Dh4YEFCxagf//+2apz8ODBWLduncF13bt3x9KlS7NVf1qqVauW5nCWS5cuRffu3XOl3awoSLHmBA4pSZQKh5QkIiIiIiIiKnyK/VBe6hAKvKh4ActZbwAAbydYMeFGukbdAKyc/nv95gkwv4x08ZDRYi1L4o7PfHgULwpVIUy45YanT58iMjLS4Dpra+sM5zDLqnv37iEhwXA3YycnJ1hZWeVKu1lRkGLlkJJEREREREREREQZkHSYtkIiJjYW9Q4OAAA86bsC5rk0xxN7IxZQ4Sf+6+EGAPeOSxdLASblvSohMQlJ0XGIt3WDXKmULI6CxNHRMdeSaunJbPInPyhIseYEJtyIiIiIiIiIiKhQEwq11CEUeCqFGhv/2Kx9nVtDZj3uzURNQWP353CY7RwFmRCAmw9w7zjErtGId6mNV00XSh1egSLpvUqWiOTJGWV5OrcaUWHChBsREREREREREZER7r29h4fRD1FMXQxulu/Xt/dzm8bcXuoQyEivmn0Nu4OjoUw1Z1t8ifp41fgraFR2EkZGRJS3mHCjbAsJCcEHH3yAqVOnIigoKMv1BAUFITg4GIcOHYKfn1+OxUdEREREREREhVdEfESetRWZEInFYYtx+dVl7bLKdpUxtPxQWCusc6VNWzPbXKmXKKdoVHZ40XIFTF/dgsnrcCTZuCLRrrTUYRER5Tkm3IiIiIiIiIiICqHYpFipQ8gTXQ93zbO25DI5LBWW+Mr3K3g7eSP0SSimnZyGQScHQSM0udLm1sZbc6VeY0VHR8O3vi8A4PCJw1Cr0x/6TmWSO3O8Uf6VaFeaiTYieq8x4UZEREREREREVAi1PdhW6hAKHY3QYEq9KQhwDwAABLgHQEBgzOExudZmfnkfNXEavHz5EkByklOuTH+Op73N9uZFWERERPkGZz8kIiIiIiIiIiLKJG8nb53XNZ1qShQJEREZcvjwYSiVSkRERGR6m+nTp6NWrVq5F1QqzZo1w6hRo/KkLcpb7OFGBsXHx+P777/Hjh07cPXqVTx9+hQ2NjZo0KABJk+ejOrVq2dYh7u7OwDg/PnzGDt2LLZt24bIyEhUrlwZkyZNQps2bdLc9rfffsPs2bMRFhYGW1tbdO7cGXPmzIG5uXmOxkhERERERERUWOWXoQhzW173AAt9Eqrt4QYAZ5+czdX28sv7GB0djcqoDADY4LshwyEliYjySrNmzVClShXMnz8fAFCvXj3cu3cPNjY2udoO0buYcCODXr58ieHDh6Nhw4Zo2bIl7Ozs8M8//2Dbtm3YvXs3jhw5kqmMf3x8PJo2bYqYmBj07dsXERERWL9+Pdq1a4eff/4ZPXv21Ntm6dKl2L17N9q2bQs/Pz/s2bMHS5YswYsXL/DLL7/keIxEREREREREhdH7MofWBt8NedbWl5e+xIxTMyAgUNOpJs4+OYuZp2aiil0VTKwyMVfazC/vo8bkvznqVCaqfBMXEdG7zMzM4OzsLHUYeoQQSEpKkjoMykVMuJFBdnZ2CA8PR/HixXWWX7lyBXXr1sUXX3yB/fv3Z1jPo0ePUL58eZw6dQoKhQIAMHLkSNSoUQNDhgxBmzZtYGVlpbPN/v37ERoairJlywIAZsyYgWrVqmHdunWYN28eihUrlqMxEhEREREREVHBZWtmm2dtTa4yGbP/nq0zZ1uNIjUwvtJ42JjlbE8KIiLK2EcffYQjR47gyJEj+OabbwAAy5cvx8CBA/HkyRPY2toCAFasWIGZM2fixYsXaNasGXx8fDBz5kw8ffpUp75ffvkFwcHBePXqFQICArBs2TJYWVkZbOf69evaUd4MOXz4MPz9/bF9+3ZMnToVly9fxo4dOwAAGo0GEyZMwKpVq2BmZoaBAwdi8uTJ2m3Dw8MxYsQIHDp0CHK5HP7+/li4cCGcnJxy8OhRTuMcbmSQUqnUS2QBQMWKFfHBBx/gyJEjSEhIyFRd06dP1ybbAKBcuXLo378/IiIisHWr/rAIw4YN0ybbAMDc3Bzdu3eHEAKhoaG5EiMRERERERERUUZszGwwq8Ys/FDvBwRVDcIP9X7ArBqzmGwjyiVJd+8i4fgJJN29K3Uo7yUhBBLikiT5EUJkKsb58+ejbt266N+/P+7du4d79+6hRIkSOmVOnDiBIUOGYPDgwfjrr7/QpEkTzJkzR6+ulNHTNm/ejM2bN+Po0aOYN29emu2ULFkyUzF+8cUXmD59Oi5evIjKlZOH5l27di0sLCxw9OhRzJw5EzNmzMCff/6pPe6dO3fGq1ev8Oeff2LXrl34559/0KtXr0y1R9JhDzdK04ULFzB37lwcO3YMjx8/1ktePX/+HC4uLunWoVAoULduXb3lDRs2xNKlS3HhwgW9G0WNGjX0yqfcJN+d6DInYiQiIiIiIiIiMoabpRvcLN2kDiNPyWQyVK1aVfs75T7NO8/B3ieayEjELlyIpIuXtMtMqlaBasQIyK2tJYws/5D/v+dWbkqM12D1mJO53o4hgfPqQaE0ybCcjY0NzMzMoFartcNIXr9+XafMt99+i4CAAIwcORIAUKZMGZw6dQq7du3SKafRaPDjjz9qR2Tr0aMHDh06lGY7mTVlyhQ0bdpUZ1nlypUxadIkAICXlxeWLVuGQ4cOoWnTpjhw4AAuX76M69eva5N6q1atQrVq1XD27FnUrFnTqPYp7zDhRgadOHECjRs3BgD4+/vDy8sLlpaWkMlk2LJlCy5evIi4uLgM6ylSpAjkcv2OlCldX1+/fq23ztBklqamyadq6jFucypGIiIiIiIiIiKpiJgYqUPIFBWAnZs2aV8XlLgLsjcdOkodgnTkcsitrFB80UKovb0RHRqKR1OnIqr/AECjyXj794D1zh05Wp9ISoLQaAAh/jvGUh5rjQbQZDK5L4Ru3Cm94zQaQKPBjRs30LZNG539qentnZxwS7WNm5sbrCwstMucnZySh5xMXW/qdjITFwDv6tV1txEClSpV0lnm7OyMZ/9v61pYGEqUKIGSxYtry5QvWxa2tra4FhaGmgY6rOQYA8/yKfOYcCODZsyYgbi4OBw7dgw+Pj46606dOoWLFy9mqp4XL15Ao9HoJd2ePHkCwHByLa9jJCIiIiIiIiKSSmSr1lKHQJT/aDRwCQ6CdfPmAJD8rxD4d8RIiQPLP3L63qFxcYGYNBEaAEn/f5YrEwK9BxXN0XYySxb+D5Iy2ZtWxMRAvH6NpFu3AACaf/8FACT98w+SrK2hiY2FePlSux4ANM+eARrNf9u8fAnTVK8BAC9eQBMfr132bjsZSYlD9eQJklJ9SUHExMA0Kkq3nqgoJMnlSLp1C5pnzyBLTNRrRyQlQTx9mun2s8KkTJlcq/t9wIQbGXT79m3Y29vrJbKio6Nx7ty5TNeTkJCAU6dOoX79+jrLjx49CgCoVq2a5DESERERERERERFR/qL29tZ9zWH08pxMJoNCIXUUGTNTKHRGRntXWQ8PnP37b51l565cyfF2ckq5UqVw//FjPHj8GCX+P3xl2O3beP3mDcqWKpXr7VPWMeFGBrm5ueHGjRu4cuUKKlasCCB5OMfRo0fj2bNnRtU1efJk7NmzB4r/352vXbuGlStXwsbGBm3bts0XMRIRERERERERSSGnh4XLLTExMfjg/72NDu3ZA3Nzc4kjKvze996P0aGh2h5uABB99qyE0eQ/OX3vSExKQmRsLORubjBRKnO07tzmVr48zl68iPsKBSwtLAAXFwCASalSMLG1xeAxY9CkWTMs2bkTrVq1QkhICPadPAmZiQlMSpcGAMjt7SFTKrWvAUDm4ACZqal22bvt2NvbG5xOKYX84UOdOLT1mptDZmOj25aFBWTW1jApXRrNPD1Redky9A8Kwlfz5iExMRFDp01Do4YNUTsbz9Mp9zHhRgZ9/vnn2LdvHxo0aIAuXbpApVIhJCQE//77L/z8/BASEpKpelxcXBAREYFq1aqhVatWeP36NdatW4fY2FgsX75cOwGllDESEREREREREUlFVlASV0Lgwf+HR4NKVXDiLsCs/tiUcaFCKjo4GI+CpwFCQF2zJqLPnsWjadNhUrUq1FOnSB1evpDT16AsMRGy+HhAJitw83iNGDECH330EapVr46YmBgsX748eYVcDsjlqN+gAb755hvMmDEDQcHBaNasGYYOHYply5b9t68pw1em3neZTOd4vNvO9evX4e7unnZgqetMp953l8kA/P777xgxYgSaNGsGuVwOf39/LFy4sMC9N+8bJtzIoNatW2Pjxo2YOXMm1q5dC7VajcaNG2Pz5s2YNm1apusxMzPD/v37MW7cOKxZswavX79G5cqVMXnyZLRp0yZfxEhERERERERERJTfyFP1iHnfqIOCEDNjhs6cbaY1vWE+cSLkNjYSRkb5UZkyZXDkyBGdZX369NF5PWDAAAwYMED7+tNPP4Wnp6f29eTJkzF58mSdbYYOHYqhQ4em2056fH19ERcXp7d8//79ess2btyo89rV1RWbNr2/SfeCSiaEEFIHQYVTSnb/7t27ksZhjBs3bkCpVGqHvyQiIiIiIiIiIiA6OhpeXl4AgJs3b0KtVkscERD5LA5RL+NhYW8G66IFawg8ypyku3eh+fch5MWLwSS9nkSUbYmJiYiMjISrqytUKpXU4eS4BQsWoGnTplCr1di7dy/GjRuHxYsXo3///lKHRvlEbGwswsPDYW1tDVPT//qqJSQkwM3NLVN1sIcbERERERERERHRey4uKjH99dH/rY+LSoSJSL98boqPTsKFXY/x/F6MdpmDmzmqtXSGmdpEsrhSKC34yDWnmLi7M9FGOeLs2bNYsGAB3rx5Aw8PDyxYsCDbybbBgwdj3bp1Btd1794dS5cuzVb9VPDw7k9ERERERERERAVCYrxG6hAKrV3zb6W7Pi7hv+TW3sW3oVRIN4ebTAaYmZsiYGAluJS2waNbrxHyyzUc+O4O8sNYXh+OLyN1CPmSqRnnniLp/Prrrzle59SpUzFixAiD66ytrXO8Pcr/mHAjIiIiIiIiIqICYfvsG1KHQPmAEIBfz3Io7e0IACjt7QghBPb9eEXiyJLxPDWs/ZRyUodAlKMcHR3h6OgodRiUjzDhRrmmIM3dRkREREREREREaZNBBmc7N+3vUnMpbaPzupiXrTSBEBER/R8TbkREREREREREVCBwqL7ck1GvLDOFCpO6rMyjaDL26NZrbQ83AHh4M0K6YN7B85SI6P3EhBsRERERERERERUInAMq97QcVVrqEDLtr43/4vC66xBCoJiXLR7ejMCR9Tfg4KZG7U7FpA6P5ykR0XuKCTciIiIiIiIiIqI88vrxQ7x5/hRWDo6wcZY+OZRCaVFwHhPW7lwcZ/94pDNnm2MpC9Ts4AKluuDsBxERFS78BCIiIiIiIiIiokIp9u0bqUPQiot6i7O//4Knt/8butHRswxqdu4JpYWlhJGlTWVppf09JiYGLVu2BADs2rUL5ubmUoUFpdoUPr1KIvJZHKJexsPC3gzWRZWSxUNERAQw4UZEREREREREVOAlxsVJHUK+tHnyKKlD0JLJ5FCq1Wg9fDxKlK+IB2FXsH/5N9g9JxhCaKQOz6DOs5dof0+IjcWNGze0vyvk0g+bqLYG1NZmAKS/BkyVTPgRUeaUKVMGQ4YMwdChQ6UOhXIYE25ERERERERERAXc7+M/lzoEyoAQGjQdOARl6zUAgP//K7Bj0RxpA0tH6vMqLjFR+/sfU0ZBacrHiql1X/iD1CEQEZHEpP8qChERERERERER0XugRPmK77yuJFEkRESUloSEBKlDoAKKX0UhIiIiIiIiIirgUg/9R//Jbz3/HoRd0fZwS379t4TRZCz1eRUdHY2Jf+wFAHSYNh9qtVqqsIiIdDRr1gyVKlWCSqXCqlWrYGZmhoEDB2Ly5MkAgPDwcIwYMQKHDh2CXC6Hv78/Fi5cCCcnJwDA9OnTsW3bNgwePBizZs3CvXv3EBMTA5VKhW+++QY7d+5ESEgIXF1d8cMPP8DBwQGffvopzp49i8qVK2PVqlXw9PQEANy+fRtjx47FX3/9haioKJQrVw7Tp09HkyZNJDs+lHeYcCMiIiIiIiIiKuA4f5Rh7afPlzoEreOrv8eBFd8CEChRvhIehP2NAyuWwdGzDHwCB0kdnkGpzyvTpCSd5TzniAo/IQRETIwkbcvMzSGTyTJdfu3atRg2bBiOHj2K06dP46OPPkK9evXQpEkTdO7cGRYWFvjzzz+RmJiIoUOHolevXti/f792+9u3b2Pjxo3YsGEDTExMtMtnzZqFuXPnYu7cuZg4cSL69OkDDw8PjBkzBiVLlsSgQYMwfPhwbN++HQAQFRWF5s2bIzg4GEqlEmvXrkWHDh1w+fJluLq65twBonyJCTciIiIiIiIiIiqUVJZWUoeg1SBwEE6sXaEzZ5tz2Qqo32sAlPkoTiKiFCImBnfq1pOkbY9TJyEzoidt5cqVMWnSJACAl5cXli1bhkOHDgEALl++jOvXr6NkyZIAgFWrVqFatWo4e/YsatasCQCIj4/HqlWrULRoUZ16+/Tpg06dOgEARo8ejUaNGuGLL76Av78/AGDIkCEYOHCgtnyVKlVQpUoV7evg4GBs3boVO3bswGeffWbsYaAChgk3IiIiIiIiIiKiXKa0tMIHnwzH68cP8eb5U1g5OMLGuZjUYWWaTCZDiRIltL8TEeUnlSrpzonp7OyMZ8+e4dq1ayhRooQ22QYA5cuXh62tLa5du6ZNuLm6uuol24DkRF4KR0dHvbYcHR0RGxuLyMhIWFtbIyoqCl9++SV27dqFR48eITExETExMbh//36O7i/lT0y4ERERERERERER5REb52IFKtGWwtzcHKdPn5Y6DCLKQzJzc3icOilZ28ZQKBS628tk0Gg0EEIY/JLAu8stLCwM1mtq+l8KJaV86rZSlmk0GgDA+PHjsX//fsyZMweenp5QqVTo3r074uPjjdofKpiYcCMiIiIiIiIiIiIiIh0ymcyoYR3zo/Lly+P+/fu4f/++tpdbWFgYXr9+jXLlyuV4e8ePH0efPn3Qtm1bAMDbt29x7969HG+H8ie51AEQERERERERERERERHltCZNmqBy5coIDAzE+fPncebMGfTv3x+NGjWCt7d3jrfn6emJLVu24OLFi7h06RL69Omj7f1GhR8TbkRERERERERERJSumJgYtGzZEi1btkRMTIzU4RARZYpMJsPvv/8OW1tbNGnSBC1atICHhwfWrl2bK+3NmzcPdnZ28PX1RYcOHdCsWTNUr149V9qi/EcmhBBSB0GUX9y4cQNKpVJvzF8iIiIiIiIiovdZdHQ0vLy8AAA3b96EuoAPM0dEuhITExEZGQlXV1eoVCqpwyHKc7GxsQgPD4e1tbXO3H0JCQlwc3PLVB3s4UZERERERERERERERESUDUy4EREREREREREREREREWUDE25ERERERERERERERERE2WCacRGi90tiYqLUIRARERERERER5SsJCQk6v6d+TUQFX2JiIoQQAKD9l+h9I4RAQkKCzjVgTL6ACTeiVMzMzBAfH4+kpCSpQyEiIiIiIiIiyjfi4uJ0fjcxMZEwGiLKaUlJSRBCaH+I3jcp53528gNMuBGlUrJkSWg0GqnDICIiIiIiIiLKV6KiouDg4AAAcHd3h4WFhcQREVFOio2Nxf3796FQKGBmZiZ1OER5TqPRwNTUFCVKlIBKpcpSHUy4EaViYmLCb2gREREREREREb3D1tYWz549kzoMIsolSUlJkMlkkMvlkMvlUodDlOfkcjlkMhkUCgUUCkXW6sjhmIiIiIiIiIiIiIiIiIjeK0y4EREREREREREREREREWUDE25ERERERERERESUrpiYGPj5+cHPzw8xMTFSh0NElCfc3d2xaNGiHKnr7t27kMlkuHDhQo7UVxAEBgaiXbt2kteRVziHGxEREREREREREaVLo9Hg8OHD2t+JiMg4JUuWxKNHj+Dg4CB1KNni7u6O4cOHY/jw4RmW/frrryGEyFS9d+/ehYeHB86fP49q1aplqQ6pMeFGRERERERERERERESUi0xMTODs7CxpDAkJCVAoFLneTlJSEmQyGWxsbLJdV07UkVc4pCQRERERERERERERERVIQgjMnTsXpUqVgrm5OapWrYqNGzdCCIGmTZuiefPm2h5SERERcHV1xcSJE7Xbb9u2DTVr1oRKpYKDgwM6dOhgsB1DQ0JGRERAJpMhJCQEAPDq1Sv07NkTRYsWhbm5Oby8vLBq1ao0tz98+DBq164NpVIJFxcXjB8/HomJidr1fn5+GDp0KMaOHQt7e3s4OzsjKCgo08dGJpPhu+++Q9u2bWFhYYEvv/wSALB9+3Z4e3tDpVKhVKlSCA4O1mk3KCgIrq6uUCqVKFasGIYOHaqN5969exgxYgRkMhlkMhkAYPXq1bC1tcWOHTtQoUIFKJVK3Lt3T284SI1Ggzlz5qB06dJQKpVwdXXFjBkzAAAeHh4AgOrVq0Mmk8HPzw+A/pCScXFxGDp0KBwdHaFSqdCgQQOcOXNGuz4kJAQymQwHDhxAzZo1oVarUb9+fVy/fj3Txy2r2MONiIiIiIiIiIiIiIh0CCEQkyjNnI3mpubaZE5GJk2ahD/++APLli2Dl5cXjhw5gl69eqFo0aJYs2YNKleujMWLF2PYsGH45JNP4OTkpE1a7dy5Ex06dMDEiRPx888/Iz4+Hjt37sxy3JMnT8bVq1exe/duODg44NatW2nOe/nvv/+iZcuWCAwMxE8//YRr165h4MCBUKlUOkm1NWvWYOTIkTh9+jROnjyJwMBA+Pj4oFmzZpmKaerUqZg1axYWLlwIExMT7N27F7169cLixYvRsGFD3L59Gx9//LG27MaNG7Fw4UKsX78eFStWxOPHj3Hx4kUAwB9//IGqVavi448/xsCBA3XaiY6OxqxZs/Djjz+iSJEicHR01ItlwoQJWL58ORYuXIgGDRrg0aNHuHbtGgDgr7/+Qu3atfHnn3+iYsWKMDMzM7g/Y8eOxaZNm7BmzRq4ublh7ty5CAgIwK1bt2Bvb68tN3HiRMyfPx9FixbFJ598gv79++P48eOZOmZZxYQbERER5aibT97g7otouBdRw8vJSupwiIiIiIiIiCgLYhJjUOfXOpK0fbrHaagV6gzLRUVFYcGCBTh48CDq1asHAChVqhSOHTuG77//Hr/++iu+//579O7dG0+ePMH27dtx/vx57bCKM2bMQLdu3RAcHKyts2rVqlmOOzw8HNWrV0fNmjUBJM93lpZvv/0WJUuWxDfffAOZTIZy5crh4cOHGDduHKZMmQK5PHmAwipVqmDq1KkAAC8vL3zzzTc4cOBAphNuPXr0QP/+/bWve/fujfHjx6Nv374Ako/X9OnTMXbsWEydOhXh4eFwdnZG06ZNoVAo4Orqitq1awMA7O3tYWJiAisrK73hMRMSEvDtt9+mefzevHmDr7/+Gt988422bU9PTzRo0AAAULRoUQBAkSJF0hx6MyoqCsuWLcPq1avRokULAMDy5cuxf/9+rFixAmPGjNGWnTFjBnx9fQEA48ePR6tWrRAbGwuVSpWp45YVTLgRERFevI2TOgQqBF5Fx2Pi5r9x+s5L7bI6HvaY0b4S7NSGv5VE+UMRS6XUIRAREREREREZ7erVq4iNjdVLPsXHx6N69eoAgM6dO2Pz5s2YNWsWli1bhjJlymjLXbhwQa+nVnZ8+umn6NixI86dOwd/f3+0a9cO9evXN1g2LCwM9erV0+nJ5+Pjg7dv3+LBgwdwdXUFkJxwS83FxQVPnz7NdEwpyb8UoaGhOHPmjHYoRyB5zrXY2FhER0ejc+fOWLRoEUqVKoXmzZujZcuW+PDDD2Fqmn46yczMTC/Wd/c3Li4OTZo0yXTs77p9+zYSEhLg4+OjXaZQKFC7dm2EhYXplE0di4uLCwDg6dOn2uOaG5hwIyLKY9HxiRkXymPeX/4pdQhUCMhlgJVKgaU9aqCWhx3O3HmFLzZfhv/CI9AIqaOj9FydFiB1CEZRm/FPWCIiIiIpqNUZ9zYhosLD3NQcp3uclqztzNBoNACSh4YsXry4zjqlMvnLpdHR0QgNDYWJiQlu3ryp24555toBoO1xljIfHJDcqyu1Fi1a4N69e9i5cyf+/PNPNGnSBIMHD8ZXX32lV58QQm/YzJS6Uy9P6Y2XQiaTafc7MywsLHReazQaBAcHG5yrTqVSoWTJkrh+/Tr279+PP//8E5999hnmzZuHw4cP68WSmrl5+sOAGnOs02Lo+KQsf3dZ6lhT1hlz3LKCTyuIiPJYhSl7pQ6BKFdoBDCzfWW0qpL8raFWVVwgIDDk1/MSR0YZKWj3pbuzW0kdAhEREdF7x8LCAlFRUVKHQUR5SCaTZWpYRylVqFABSqUS4eHh2uED3zVq1CjI5XLs3r0bLVu2RKtWrdC4cWMAyb2gDhw4gH79+mXYVsqQh48ePdL2nrtw4YLBcoGBgQgMDETDhg0xZswYgwm3ChUqYNOmTTrJohMnTsDKykoveZiTatSogevXr6N06dJpljE3N0ebNm3Qpk0bDB48GOXKlcPly5dRo0YNmJmZISkpyeh2vby8YG5ujgMHDuCjjz7SW58yZ1t6dZcuXRpmZmY4duwYevToASA56Xn27FkMHz7c6JhyGhNuRERElGNqedjpvK7tYZ9GSSIiIiIiIiKi7LGyssLo0aMxYsQIaDQaNGjQAJGRkThx4gQsLS3h4OCAlStX4uTJk6hRo4Z27rJLly7Bzs4OU6dORZMmTeDp6Ylu3bohMTERu3fvxtixY/XaMjc3R926dTF79my4u7vj+fPnmDRpkk6ZKVOmwNvbGxUrVkRcXBx27NiB8uXLG4z9s88+w6JFi/D5559jyJAhuH79OqZOnYqRI0dqe9PlhilTpqB169YoWbIkOnfuDLlcjkuXLuHy5cv48ssvsXr1aiQlJaFOnTpQq9X4+eefYW5uDjc3NwDJ89IdOXIE3bp1g1KphIODQ6baValUGDduHMaOHQszMzP4+Pjg2bNnuHLlCgYMGABHR0eYm5tjz549KFGiBFQqFWxsbHTqsLCwwKeffooxY8bA3t4erq6umDt3LqKjozFgwIAcP1bGYsKNiCiP5ceh2wpa7xbKv87ceaXt4QYAf6Waz43yr/x4XyIiIiIiIiLKjOnTp8PR0RGzZs3CP//8A1tbW9SoUQMTJkxA165dERQUhBo1agAApk6din379uGTTz7Bhg0b4Ofnh99//x3Tp0/H7NmzYW1tjUaNGqXZ1sqVK9G/f3/UrFkTZcuWxdy5c+Hv769db2ZmhgkTJuDu3bswNzdHw4YNsX79eoN1FS9eHLt27cKYMWNQtWpV2NvbY8CAAXpJvJwWEBCAHTt2YNq0aZg7dy4UCgXKlSun7XVma2uL2bNnY+TIkUhKSkLlypWxfft2FClSBAAwbdo0DBo0CJ6enoiLi9MZYjMjkydPhqmpKaZMmYKHDx/CxcUFn3zyCQDA1NQUixcvxrRp0zBlyhQ0bNgQISEhenXMnj0bGo0GvXv3xps3b1CzZk3s3bsXdnZ2emXzmkwYczSIiKhQevE2TuoQqBD47JdzuPn0Laa1rYjaHvb4685LTNl6BV6Olvi2Zw2pw6N0FLFUSh0CEREREeVzsbGx6NixIwBg06ZNUKlUEkdERDkpNjYWd+7cgYeHB69vei/lxDXAHm5ERMSH7ZQjlvXyxrD153XmbGvo5YCvu1WHvYWZhJEREREREVF2JSUlYdeuXdrfiYiISBcTbkRERIXJ02vAy38A+1KAY7k8bdrewgw/D6iDm0/e4O6LaLgXUcPLySpPYyAiIiIiIiIieh/88ssvGDRokMF1bm5uuHLlSh5HREy4ERFRzol6LnUE76/ol8CO4cC94/8tc/MBWi8C1PZ5GoqXJeBlaQIgDojicKVGscjcRMNERERERERE9H5r06YN6tSpY3CdQqHI42gIYMKNiEga8VFSR5A75nlKHcH7SyYHlNZA59WAa30g/ASwfTjwbR1AaKSOjjLri4dSR5A/mFlIHQERERERERFRvmZlZQUrK44slJ8w4UZEJIWZxaSOgAoboQE+XARUbJ/8umJ7QAhgYz9JwyIj8d6QLOi11BEQERERERERERlFLnUARERElENc6+u+dvORJg4iIiIiIiIiIqL3DHu4ERFJobAOG8feOdIKP/FfDzdAdz43KhgK672BiIiIiIiIiKiQY8KNiEgKhXV+ojG3pY7g/fVbH2Dn6ORhJN18kpNtu8YAbg2ALmukjo4yq7DeG4iIiIiowLOwsIAQQuowiIiI8i0m3IiIKOdYOEgdwfury8/ApgG6c7aV+gDouAKwKCJdXERERERERERERO8BzuFG+UZQUBBkMhlCQkKkDoWIqOCxKAL02QJ8dhroti753z5bmGyjPHU74jYOhR/C7Qj2diUiIiIiIqK84efnh+HDh0sdRrYFBgaiXbt2UodB2cAebkRERIWJY7nkH6Ic8jL2ZYZlIuIiMO3kNIQ+CdUu83byxpR6U2CrtE1zO3uVfU6ESERERER5IDY2Fr179wYA/Pzzz1CpVBJHRERElL8w4UZERERUyEQnROdYXb4bfDMsI5fJYamwxFe+X8HbyRuhT0Ix7eQ0tN/aHhqhSXO70z1O51ic71Ir1LlWNxEREdH7KCkpCRs3bgQArF69WtpgiIhyWUJCAhQKhdRhUAHDhBsRERFRIVPn1zp52p5GaDCl3hQEuAcAAALcAyAgMObwmHS3y804L/e9nGt1ExERERERUf6i0WgwduxY/PjjjzAzM8Mnn3yCoKAg7frw8HB8/vnnOHDgAORyOZo3b44lS5bAyckJQPJ0R1u2bMHQoUPx5Zdf4u7du0hKSsKmTZsQHByMW7duQa1Wo3r16ti6dSssLCwAAKtWrcLcuXNx584duLu7Y+jQofjss8/SjHPjxo3p1gcAX331FebPn4/4+Hh069YNixYt0ib/Xr16hWHDhmH79u2Ii4uDr68vFi9eDC8vL+32J06cwPjx43HmzBk4ODigffv2mDVrlk4blDs4hxvlqZCQEMhkMgQFBeHkyZMICAiAra0tZDKZTrnffvsNNWrUgLm5OVxcXDB06FDExMQYrHPNmjWoW7cuLC0tYWlpibp162LNmjV5sTtERET0f95O3jqvazrVlCgSIiIiIiIiyhFCAPFR0vwIYVSoa9asgYWFBU6fPo25c+di2rRp2L9///93Q6Bdu3Z4+fIlDh8+jP379+P27dvo2rWrTh23bt3Cb7/9hk2bNuHChQt4/Pgxunfvjv79+yMsLAwhISHo0KEDxP9jW758OSZOnIgZM2YgLCwMM2fOxOTJk9N8Nv3o0aN06wOAQ4cO4fbt2zh06BDWrFmD1atX6/QqDgwMxNmzZ7Ft2zacPHkSQgi0bNkSCQkJAIDLly8jICAAHTp0wKVLl7BhwwYcO3YMQ4YMMep4UtbIhDDyzCXKhpCQEHzwwQdo1qyZ9veqVasiPDwc5cqVQ3BwMDp16oTdu3ejbdu2cHJywp49exAWFoYePXrgl19+0alvxIgRWLRoEYoXL46OHTtCJpNh06ZNePDgAUaMGIEFCxZItKdERETSyckhJTPbC+0r36+0PdwAYM/dPRn2cOOQkkREREQFR1RUFCwtLQEAb9++ZU8JokImNjYWd+7cgYeHx39zNMZHATOLSRPQFw8Bs8zdZ/z8/JCUlISjR49ql9WuXRuNGzfG7NmzsX//frRo0QJ37txByZIlAQBXr15FxYoV8ddff6FWrVoICgrCzJkz8e+//6Jo0aIAgHPnzsHb2xt3796Fm5ubXruurq6YM2cOunfvrl325ZdfYteuXThx4oRe+YzqCwwMREhICG7fvg0TExMAQJcuXSCXy7F+/XrcvHkTZcqUwfHjx1G/fn0AwIsXL1CyZEmsWbMGnTt3Rp8+fWBubo7vv/9eW++xY8fg6+uLqKgozr+ZDoPXgJE4pCRJYv/+/VixYgX69++vXZbSxXf//v0IDQ1F2bJlAQAzZsxAtWrVsG7dOsybNw/FiiXf5I8ePYpFixahfPnyOHnyJGxsbAAAwcHBqFu3LhYuXIgOHTqgQYMGebtzREREEsvJZNPhroczLDMyZCRmnJoBAYGaTjVx9slZzDw1EzWdamK+3/w8iZOIiIiIiIjeX1WqVNF57eLigqdPnwIAwsLCULJkSW2yDQAqVKgAW1tbhIWFoVatWgAANzc3bbINAKpWrYomTZqgcuXKCAgIgL+/Pzp16gQ7Ozs8e/YM9+/fx4ABAzBw4EDtNomJidrn1O9Kr74UFStW1CbbUvbj8uXL2v0wNTVFnTr/fTG2SJEiKFu2LMLCwgAAoaGhuHXrlk7HFSEENBoN7ty5g/Lly2fyiFJWMOFGkqhevbpOsi21YcOGaZNtAGBubo7u3bsjODgYoaGh2oRbSlfaoKAgnZuYjY0Npk6diu7du2P16tVMuBGR5OJu3UJ8eDjMXF2hLF1a6nCIjGKvss+wzEK/hRh3ZJxOj7Z6LvUwp9Ec2Kns0tmSiIiIiIiI8i2FOrmnmVRtG1P8/3OcpZDJZNBoNACSE07vTmlkaPm7PXdNTEywf/9+nDhxAvv27cOSJUswceJEnD59Gmp1cnzLly/XSYClbGdIevV5eHhkaj8MSb0fGo0GgwYNwtChQ/XKubq6Gtyecg4TbiSJ2rVrp7muRo0aestKlCgBAIiIiNAuO3/+PIDkLsPvSll24cKFLMdI9D5LfPlS6hAKhaSICDyaOhUxZ85ql5nXqgmX4GCY2NpKF1gBY2qfccKHpGWnssMP/j/gdsRthEeGw9XaFZ62nlKHRURERERERNkhk2V6WMf8rEKFCggPD8f9+/d1hpR8/fp1hj2+ZDIZfHx84OPjgylTpsDNzQ2bN2/GyJEjUbx4cfzzzz/o2bNnpmNJr77M7EdiYiJOnz6tM6TkjRs3tPtRo0YNXLlyBaX5hW9JMOFGknByckpznaEut6amyadqUlKSdllkZCTkcrlON9/U9cvlcrx+/ToHoiUA0ETn3HxAlP/drO8jdQiFg1wOuZUVii9aCLW3N6JDQ/Fo6lT80/pD4P/fTqKMlT0XKnUI7y252rhvFHraejLRRkRERFRIqdVqvH37Vvs7EVFB0bRpU1SpUgU9e/bEokWLkJiYiM8++wy+vr6oWbNmmtudPn0aBw4cgL+/PxwdHXH69Gk8e/ZMm9wKCgrC0KFDYW1tjRYtWiAuLg5nz57Fq1evDCbQMqovI15eXmjbti0GDhyI77//HlZWVhg/fjyKFy+Otm3bAgDGjRuHunXrYvDgwRg4cCAsLCwQFhaG/fv3Y8mSJVk4emQMJtxIEoa68BrL2toaGo0Gz549g6Ojo866p0+fQqPRwNraOtvtULLrNbylDoGo4NFo4BIcBOvmzQEg+V8h8O+IjL+1RP/h/Uc65a+FSR0CEREREeUTMplMb7g1IqKCQCaTYcuWLfj888/RqFEjyOVyNG/ePMMElLW1NY4cOYJFixYhMjISbm5umD9/Plq0aAEA+Oijj6BWqzFv3jyMHTsWFhYWqFy5MoYPH56l+jJj1apVGDZsGFq3bo34+Hg0atQIu3bt0g5FWaVKFRw+fBgTJ05Ew4YNIYSAp6cnunbtmuk2KOuYcKMCq3r16jh//jxCQkLQpUsXnXWHDx8GAFSrVk2CyIiI/qP21k0WqdP55hQRERERERERERknJCREb9mWLVt0Xru6umLr1q1p1hEUFISgoCCdZeXLl8eePXvSbbtHjx7o0aNHpuLMqL7Vq1frLVu0aJHOazs7O/z000/ptlOrVi3s27cvUzFRzmLCjQqsvn37YuXKlQgODkbz5s21vdkiIyMRHBysLUM5g0O6vV/YoyjnRIeGanu4AUD02bPplCZDeP8hIiIiIpJeXFwcBg0aBAD4/vvvoVQqJY6IiIgof2HCjQqsRo0a4fPPP8eSJUtQqVIldOzYEUII/PHHH7h//z6GDh2KRo0aSR1moWHsPD5UsHmdOC51CIXCg2HD8HjadEAIqGvWRPTZs3g8/Uuoa9VC8a8XSR1egcH7DxERERGR9BITE7FmzRoAwNKlS5lwIyIiegcTblSgLV68GNWrV8eyZcvwww8/AAAqVqyI4OBg9OvXT+LoiAouU3t7qUMoFEosXoyHo0brzNlmUb8+is3/CqZ2dhJGRkRERERERERERDlJJoQQUgdBRERUmMXduoX48HCYubpCWbq01OGQBF4+jMLrZ9GwKaqGfTFONE9EREREBU9UVBQsLS0BAG/fvoWFBf+uJSpMYmNjcefOHXh4eEClUkkdDlGey4lrgD3ciIiIcpmydGkm2vKBmDfxed5mbFQCQn65joc3I7TLinnZwq9nWagsFHkWh7mVWZ61RURERERERET0PmLCjYiIiJAQlyR1CLlu5Zhjed6mTAaYmZsiYGAluJS2waNbrxHyyzWsCz6NvBxj4OOvffOusVyiUJpIHQIRERERERERUZqYcCMiIiL8MOyw1CEUSkIAfj3LobS3IwCgtLcjhBDY9+OVPI2jMLy/g79rLHUIRERERERERERpkksdABEREVFh5lLaRud1MS9baQIhIiIiIiIiIqJcwx5uREREVCiGHMyIVL28Ht16re3hBkBnPre88j68v0RERESUu9RqNZ4+far9nYiIiHQx4UZERETvxfxY/ec1yPM29/zwNw6vuw4hBIp52eLhzQgcWX8Dxbxs0fzjSnkWx/vw/hIRERFR7pLJZChatKjUYRAR5Sg/Pz9Uq1YNixYtAgC4u7tj+PDhGD58eJ7GERgYiIiICGzZsiVT5e/evQsPDw+cP38e1apVy9XYKPOYcCMiIqL3grmVWZ632XxQJexfcUVnzraS5e3QbEBFmFvmfTxERERERERE9J8//vgDCoVC6jCokGDCjYiIiCiXmFuaoc2w6nj5MAqvn0XDpqga9sUspA4rW148CMerx49g5+yCIiVcpQ6HiIiIiPJIXFwcRo4cCQBYsGABlEqlxBEREWWfvb291CFQISKXOgAiIiKiws6+mAU8qhbN82RbdOTrHPt58e8DbAgaj9WjPsPWedOxetRn2BA0Hi/+fWB0XURERERU8CQmJuLbb7/Ft99+i8TERKnDISLS2rhxIypXrgxzc3MUKVIETZs2RVRUFAIDA9GuXTsEBwfD0dER1tbWGDRoEOLj47Xb+vn5pTt85KpVq2BjY4P9+/cDAK5evYqWLVvC0tISTk5O6N27N54/f56tOA3Zs2cPGjRoAFtbWxQpUgStW7fG7du39cpdu3YN9evXh0qlQsWKFRESEpKpWCh3sIcbERERkZESYmOlDiFTlg3smWN1yWRyKNVqtB4+HiXKV8SDsCvYv/wbrBn1GYTQGFXX0DUbcyyujChUqjxri4iIiIiIqDARQiAmIUmSts0VJpDJZBmWe/ToEbp37465c+eiffv2ePPmDY4ePQohBADgwIEDUKlUOHToEO7evYt+/frBwcEBM2bMyLDur776CrNmzcLevXtRt25dPHr0CL6+vhg4cCAWLFiAmJgYjBs3Dl26dMHBgwezFee7oqKiMHLkSFSuXBlRUVGYMmUK2rdvjwsXLkAu/68f1ZgxY7Bo0SJUqFABCxYsQJs2bXDnzh0UKVIkw/2jnMeEGxEREZGRFvftJHUIeU4IDZoOHIKy9RoAwP//FdixaI7RdeXl8Ru1YUeetUVERERERFSYxCQkocKUvZK0fXVaANRmGacvHj16hMTERHTo0AFubm4AgMqVK2vXm5mZYeXKlVCr1ahYsSKmTZuGMWPGYPr06TqJq3dNmDABa9asQUhIiLa+ZcuWoUaNGpg5c6a23MqVK1GyZEncuHEDZcqUyXKc7+rYsaPO6xUrVsDR0RFXr15FpUqVtMuHDBmiLbts2TLs2bMHK1aswNixY9Osm3IPh5QkIiIiokwpUb7iO68rpVGSiIiIiIiIKPdVrVoVTZo0QeXKldG5c2csX74cr1690lmvVqu1r+vVq4e3b9/i/v37adY5f/58fP/99zh27JhOUiw0NBSHDh2CpaWl9qdcuXIAYHC4R2PifNft27fRo0cPlCpVCtbW1vDw8AAAhIeH65SrV6+e9ndTU1PUrFkTYWFh6cZCuYc93IiIiIiMlJdDImZHTvckexB2RdvDLfn131mqp6AcPyIiIiIioveZucIEV6cFSNZ2ZpiYmGD//v04ceIE9u3bhyVLlmDixIk4ffp0utulN1xlw4YNsXPnTvz2228YP368drlGo8GHH36IOXP0R3pxcXHJcpwpybTUPvzwQ5QsWRLLly9HsWLFoNFoUKlSJZ3557Kyb5S7mHAjIiIiMlJBmRfs0+W/5Fhd2xfMwoGVywAIlChfCQ/C/saBld+hRIVK+HDEBKPqKijHj4iIiIiI6H0mk8kyNayj1GQyGXx8fODj44MpU6bAzc0NmzdvBgBcvHgRMTExMDc3BwCcOnUKlpaWKFGiRJr11a5dG59//jkCAgJgYmKCMWPGAABq1KiBTZs2wd3dHaamxh+XtOIcOXKkTrkXL14gLCwM33//PRo2bAgAOHbsmME6T506hUaNGgEAEhMTERoaiiFDhhgdG+WM/H+1EBEREVGWqK1tcqyuD0dOwK4lX+nM2eZWpTpafj46R9shIiIiIiIiyqzTp0/jwIED8Pf3h6OjI06fPo1nz56hfPnyuHTpEuLj4zFgwABMmjQJ9+7dw9SpUzFkyJB0528Dkodq3L17N5o3bw5TU1OMGDECgwcPxvLly9G9e3eMGTMGDg4OuHXrFtavX4/ly5fDxCTtXnnpxfkuOzs7FClSBD/88ANcXFwQHh6u09MutaVLl8LLywvly5fHwoUL8erVK/Tv39+4g0g5hgk3IiIiIsqQ2toGnSZOx4sH4Xj1+BHsnF1QpISr1GERERERUR4xNzfHnTt3tL8TEeUH1tbWOHLkCBYtWoTIyEi4ublh/vz5aNGiBTZs2IAmTZrAy8sLjRo1QlxcHLp164agoKBM1e3j44OdO3eiZcuWMDExwdChQ3H8+HGMGzcOAQEBiIuLg5ubG5o3b55hAi+9ON8ll8uxfv16DB06FJUqVULZsmWxePFi+Pn56ZWdPXs25syZg/Pnz8PT0xNbt26Fg4NDpvaPcp5MCCGkDoKIiIiIiIiIiIiIiKQRGxuLO3fuwMPDA6pCMg1AYGAgIiIisGXLFqlDoQIgJ66B9NOuRERERERERERERERERJQuJtyIiIiIiIiIiIgoXfHx8RgzZgzGjBmD+Ph4qcMhIspXwsPDYWlpmeZPeHi41CFSHuCQkkRERERERERERJSuqKgoWFpaAgDevn0LCwsLiSMiopxUGIeUzEuJiYm4e/dumuvd3d1hamqadwGR0XLiGuA7TERERERERERERERElEWmpqYoXbq01GGQxDikJBEREREREREREREREVE2MOFGRERERERERERERETgDFT0vsqJc58JNyIiIiIiIiIiIiKi95hCoQAAREdHSxwJkTRSzv2UayErOIcbUSpJSUnQaDRSh0FERERERERElK8kJCTo/J76NREVDlZWVnjy5Ak0Gg3UajVkMpnUIRHlOiEEoqOj8ezZM1hbW0Oj0ejlCDKbhGPCjSiV+/fvIz4+XuowiIiIiIiIiIjyldS9Xu7evQu1Wi1hNESUG4QQSEpKwsOHD5lso/eKEAJyuRxRUVG4c+eO3voyZcpkqh4m3IhSiY+Ph4mJCUxNeWkQEREREREREaVQKBTYt28fAMDGxgZyOWeqISqMVCoVNBoNhBCcz43eCzKZDDKZLM3PtcTExEzXxawC0TtMTU2zNU4rEREREREREVFhVLFiRalDICIiyrf4VRQiIiIiIiIiIiIiIiKibGAPNyIiIiIiIiIiIkpXfHw8lixZAgD4/PPPYWZmJnFERERE+YtMcCBWIq0bN25AqVRySEkiIiIiIiIiolSio6Ph5eUFALh58ybUarXEEREREeW+hIQEuLm5Zaosh5QkIiIiIiIiIiIiIiIiygYm3IiIiIiIiIiIiIiIiIiygQk3IiIiIiIiIiIiIiIiomxgwo2IiIiIiIiIiIiIiIgoG0ylDoCIiIjozosYPHgdjxI2ZvAoYi51OEREREREREREREZhwo2IiCiTXkUnSB1CofM6NhHzDj3A+X/fapdVL26JMR+UgI2Kf6bkBTu1QuoQiIiIiIiIiIgKPD7JIiKi91ZMQpJR5Vv9+HcuRfL+kssAK5UCS3vUQC0PO5y58wpfbL6MXr9cg0ZIHd374cCnVaQOocAwV5hIHQIRERGRZJRKJXbu3Kn9nYiIiHTJhBB8nEX0fzdu3IBSqYRCwW/7E70P6i8+L3UIBGBpjxpoVcVF+3rHpYcY8ivfG8p/TgytLnUIRERERERERJSHEhIS4Obmlqmy8lyOhYiIiChdtTzsdF7X9rCXKBIiIiIiIiIiIqKs4ZCSRET03jJ2KL0myy7lUiTvtzN3Xun0cPvrzksJo3n/cEhJIiIiIsqM+Ph4rFixAgAwYMAAmJmZSRwRERFR/sIhJYlS4ZCSRJSeV9EJUodQ6EzafRf3IuIwrW0l1Pawx193XmLK1r/hZqvEly3cpQ7vvWCn5mceEREREWUsOjoaXl5eAICbN29CrVZLHBEREVHuM2ZISfZwIyIiyqT3PTFh+uoWTF6HI8nGFYl2pXOkzhktPRC0967OnG21Xa0QFOAOW3P+mUJERERERERERAUDn2QRERFlkzymcA+BKIuNgO2xICgfndEui3OphYgGQRAq22zVbQ9gcYA97ryyxP3IRJS0NoWHnRmASCAmW1XnSxpzzk9HRERERERERFQYMeFGRETvDVlCdK7U6/yzT67Um2/I5IDSGui8GnCtD4SfgHL7cDht/BAQmhxpwhlAvRypKX971C9U6hAKLaHgkEZEREREREREJB0m3IiI6L3hsspb6hAKJqEBPlwEVGyf/Lpie0AIYGM/ScMqiHgO5p6HH4dJHQIRERERERERvcfkUgdAREREBYBrfd3XboW8Vx8REREREREREZER2MONiIjeG7k1nN970Wsp/MR/PdwA4N5x6WIpwDikJBERERERERFR4cSEGxERvTdya46nx70Ld/LJ7s/hMNs5CjIhknu23TsOsWs04l1q41XThVKHV6BwnjEiIiIiKqiUSiV+//137e9ERESkSyaEEFIHQZRf3LhxA0qlEgqFQupQiIjyDXnsK9gdHA3lgxPaZXEl6uNV46+gUdlJGBkREREREREREVHuSUhIgJubW6bKGj2HW0hICGQyGYKCgozdtFBKOR4pP3Xr1tUrc/36dbRt2xZOTk6QyWRwd3fP+0DfIyVKlNB5T+7evSt1SEREBZpGZYcXLVfgaefteOG/NPnfliuYbKMsuff2Hk4+PYl7b+9JHQoRERERERERUY7Jd0NKpiSjpEiSJCUl4aeffsKvv/6KCxcu4PXr17C3t0etWrUQGBiIjh07prmtr68v/Pz8UKJECb0627dvj3/++Qe9e/dG8eLFYWtrm8t7kkzKY5nT9u7di1mzZuHcuXMQQsDb2xsTJkxAQECAXtmRI0ciMjISW7ZswcWLFyWIloiocEq0K41Eu9JSh0F5KCI+IsfqikyIxOKwxbj86rJ2WWW7yhhafiisFdY51g4A2JrZ5mh9RERERJT8Df9ffvkFANCzZ0+ODkRERPSOfJdwk8rTp0/Rtm1bnDp1Ci4uLmjbti0cHR3x4MED7Ny5Ezt27MCHH36IdevWwcLCQm97Pz8/g73+7ty5g7CwMAwaNAjfffddHuxJ4fPLL7+gV69ecHBwQN++fSGTyfDbb7+hefPmWLt2LXr27KlTfuTIkQCSE41MuBERUUEQmxQrdQgGdT3cNcfqksvksFRY4ivfr+Dt5I3QJ6GYdnIaBp0cBI3Q5Fg7ALC18dYcrS+vqUxUUodAREREpCchIQETJ04EAHTp0oUJNyIioncw4QYgMTER7dq1w6lTpzBgwAAsWbIE5ubm2vURERHo1asXtm/fjv79+2PDhg2Zrvvhw4cAAGdn5xyP+33w6tUrDBkyBA4ODjh37hxKliwJAJgwYQJq1KiBIUOGoGXLlrCz47BmRERUcLU92FbqEHKdRmgwpd4UBLgn904PcA+AgMCYw2NyvK2Cfjz3NtsrdQhEREREREREZCSj53BL7ciRI/D19YWlpSXs7e3Ro0cPPHjwQK/c06dPMWLECJQuXRpKpRIODg7o2LEj/v77b22Zu3fvQiaT4d69e7h3757OHFwpPcfi4+OxZMkSBAQEoGTJklAqlXB0dESHDh1w/vz5LO/HmjVrcPLkSTRs2BDLly/XSbYBgK2tLX7//XeULl0av/32Gw4ePJipet3d3eHr6wsACA4O1u7P6tWrtWXevHmDqVOnomLFijA3N4etrS2aN2+OY8eO6dUXGhqKIUOGoFKlSrCxsYG5uTkqV66M2bNnIyEhQVsuM8dy9erVerGkSGuePplMBj8/P/z7778IDAyEs7Mz5HI5QkJCtGWOHDmCDz/8EA4ODlAqlfDy8sKkSZMQHR2dqWP2rt9//x0RERH4/PPPtck2AHBxccHw4cMRERGB33//PUt1ExERUd7ydvLWeV3TqaZEkRARERERERER5aws93A7deoUZs2ahVatWmHo0KE4d+4c1q1bh2PHjuHMmTNwcnICANy+fVubpPH390e7du3w9OlTbNq0CXv37sWBAwdQp04d2NraYurUqVi0aBEAYPjw4dq2/Pz8AAAvX77E8OHD0bBhQ22vpn/++Qfbtm3D7t27ceTIEdSqVcvofVm5ciUAYOLEiZDJZAbLmJubY9SoUfj000+xcuVKNG7cOMN6hw8fjgsXLmDNmjXaOd4AoFq1atr9adSoEa5cuYKGDRsiICAAr1+/xtatW/HBBx/g999/R7t27bT1LV++HNu3b0ejRo3QsmVLREdHIyQkBBMmTMCZM2ewadMmAMjUscyqFy9eoF69erC3t0fXrl0RHx8Pa+vkeVe+++47fPbZZ7Czs8OHH36IokWL4syZM5gxYwYOHTqEQ4cOwczMzKj2UpJ5/v7+eusCAgIwfvx4HD58GB9//HG29ouIiEhK+XUIxJzuKRb6JFTbww0Azj45m6P1p8ivx5OIiIiIiIiICq8sJ9z27t2LH3/8EQMGDNAumzZtGqZOnYovvvgCK1asAAD06dMHjx8/xt69e9GsWTNt2UmTJqFmzZoYOHAgLl26BFtbWwQFBWl7XBmaD83Ozg7h4eEoXry4zvIrV66gbt26+OKLL7B//36j9iMxMRFnzpyBqamptjdaWpo0aQIAOHnyZKbqHj58OEJCQrBmzRqDc7x9/vnnuHLlClauXIl+/fppl8+cORO1atXCxx9/jObNm0OlSp7HY8KECVi6dClMTEy0ZYUQ+Oijj7By5UocP34cPj4+mTqWWfX333+jX79+WL58uU4cV69exeeff45q1arhzz//hL29vXbd7NmzMWHCBCxZsgSjRo0yqr2bN28CALy8vPTWpSxLKUNERFRQ5dc5uzb4Zn4Y7Yx8eelLzDg1AwICNZ1q4uyTs5h5aiaq2FXBxCoTc6wdIP8eTyIiIiIiIiIqvLI8pGTZsmXRv39/nWVjxoxB0aJFsW7dOsTHx+P8+fM4ceIE+vbtq5NsA4AyZcpg4MCBuHz5ss7QkulRKpV6yTYAqFixIj744AMcOXJEZ2jFzHjx4gUSEhLg4OCgTWylJWVIw0ePHhnVhiHPnz/Hhg0b0KRJE51kGwA4OTlhzJgxePbsGf7880/tcjc3N50kF5A8zOPgwYMBQKdsbjEzM8PcuXP14vj++++RmJiIxYsX6yTbAGDs2LHa88JYr1+/BgDY2NjorbOwsICJiYm2DBEREeUsWzPbHPuZXGUyPCw9MObwGHzw2wcYc3gMPCw9MKnKpBxtx9bMVurDRkRERERERETvoSz3cPPx8dEbftHc3Bze3t7Ys2cPbty4gVOnTgEAHj9+bLCX1bVr17T/VqpUKVPtXrhwAXPnzsWxY8fw+PFjvQTb8+fP4eLikoU9yry0hp00xpkzZ5CUlITY2FiDxyal19a1a9fQunVrAMlz2H3zzTdYv349rl27hrdv30IIod3m4cOH2Y4rIx4eHnBwcNBbnvJe79mzx2DiT6FQaN9vIiLKX5Lu3oXm34eQFy8GE3d3qcOhQsrGzAazaszCvbf38DD6IYqpi8HN0k3qsIiIiIiIiIiIckSWE26Ojo4Gl6fM3fb69Wu8fPkSALBz507s3LkzzbqioqIy1eaJEye0c6f5+/vDy8sLlpaWkMlk2LJlCy5evIi4uDhjdgNFihSBQqHA8+fPERsbm24vt/v37wMAnJ2djWrDkJRjc/z4cRw/fjzNcqmPTadOnbB9+3aUKVMGXbt2haOjIxQKBSIiIvD1118bve9ZkfL+vitlf2bMmJGj7aX0bHv9+jWKFCmisy4qKgpJSUkGe78REeUlTUSE1CFkiSYyErELFyLp4iXtMpOqVaAaMQLy/8/PWZDIbW2lDoEywc3SjYk2IiIiogLIzMwMa9as0f5OREREurKccHv69KnB5U+ePAGQnCix/v/DuiVLlmDIkCFZbUprxowZiIuLw7Fjx+Dj46Oz7tSpU7h48aLRdZqamqJWrVo4ceIEDh8+jICAgDTLHjhwAABQr149o9t5V8qxGTVqFL766qsMy585cwbbt29HQEAAdu7cqTOk46lTp/D1118b1b5cnjyaaGJiot669IZoTKt3X8r+REZGwsrKyqhY0uPl5YWzZ8/i5s2begm39OZ3IyqIREyM1CFQFr3p0FHqELJGLofcygrFFy2E2tsb0aGheDR1KqL6DwA0GqmjM5r1zh1Sh/DekJmbSx0CEREREeUxU1NTNG3aVOowiIiI8q0sJ9yOHz8OIYROAiYmJgahoaEwNzdHmTJlEB0dDQA4efJkphNuJiYmiI+PN7ju9u3bsLe310u2RUdH49y5c1ncEyAwMBAnTpzArFmz4O/vbzCpFBsbiwULFgCA3tx1WVGrVi3IZDKcPHkyU+Vv374NAGjVqpXe/GlHjx41uE16x9LOzg4A8O+//+qtO3/+fKZiSq1OnTo4d+4cTp06pTdfX3b4+vpi3bp12LdvH+rWrauzbu/evdoyRIVBZKvWUodA7xuNBi7BQbBu3hwAkv8VAv+OGClxYFnDayjv2Bw8IHUIRERERERERET5ijyrG16/fh0rV67UWTZv3jw8e/YM3bt3h5mZGWrXro06depg3bp12LBhg14dGo0Ghw8f1llmb2+vHd7xXW5ubnj16hWuXLmiXZaUlITRo0fj2bNnWd0VBAYGok6dOjh8+DA++eQTvbZfv36Nrl274ubNm+jcubN2WMvscHZ2RpcuXXDixAnMmzdPZy62FKdPn9YmLd3ckodeOnbsmE6ZK1euYNasWQbbSO9Y1qhRAzKZDOvXr9dZf/PmTaN7ywHAZ599BlNTU3z++efaoTdTi4iIyFIir0uXLrCxscGSJUt06n306BEWLVoEW1tbdO7c2eh6iYgomdrbW/d1zZoSRUJERERERPlZQkICNmzYgA0bNiAhIUHqcIiIiPKdLPdw8/f3x2effYadO3eiXLlyOHfuHPbu3YuSJUti5syZ2nLr1q3DBx98gG7dumHRokXw9vaG6n/s3XtclGX+//H3DOcBkYMpqAlsorliapBotUq2pWmUpq1+t0ysrLZfW2a567c0wA5Wa6YdVrOtsL6uVrqKq5XbqqB5ylObth6QRFPEEwcVkOP8/jAnRhBBZ7gHeD0fDx/Mfd3XfV1vwNTmw3Vd3t46ePCgNmzYoOPHj9sVfPr3768tW7YoPj5ev/nNb+Tp6ambb75ZN998s/74xz/qX//6l26++Wb97ne/k7e3t9LS0nT48GHFxcUpLS3tsj4XDw8Ppaam6q677tKcOXO0bNkyDRo0SK1bt9bhw4e1bNkynTx5Unfeeac++uijy/2SVfPXv/5Ve/bs0Z/+9Cd98skn6tOnj1q2bKmffvpJW7duVUZGho4cOSKLxaJevXqpV69e+uyzz3TkyBH17t1bBw8e1NKlSzV48GAtXLiw2vi1fS3btWunESNGaMGCBYqOjtbAgQN17NgxLV68WAMHDtSiRYvq9blERUXpr3/9q/7whz+oc+fOGjRokK655hqdOnVKP/74o9LT05WQkKDZs2fXa9zAwEC98847GjVqlK6//nqNHDlSZrNZn376qY4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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = az.plot_forest(\n",
+    "    [\n",
+    "        idata_confounded[\"spike_and_slab\"],\n",
+    "        idata_confounded[\"horseshoe\"],\n",
+    "        idata_confounded[\"exclusion\"],\n",
+    "        idata_confounded[\"normal\"],\n",
+    "        idata_confounded[\"rho_tight\"],\n",
+    "        idata_confounded[\"rho_tight_spike_slab\"],\n",
+    "    ],\n",
+    "    var_names=[\"alpha\", \"rho\", \"beta_O\", \"beta_T\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\n",
+    "        \"spike_slab\",\n",
+    "        \"horse shoe\",\n",
+    "        \"exclusion_restriction\",\n",
+    "        \"normal\",\n",
+    "        \"tight_rho\",\n",
+    "        \"tight_rho_spike_slab\",\n",
+    "    ],\n",
+    "    figsize=(20, 15),\n",
+    ")\n",
+    "\n",
+    "ax[0].axvline(3, linestyle=\"--\", color=\"k\")\n",
+    "ax[0].set_title(\n",
+    "    \"Comparing Parameter Estimates across Model Specifications\", fontsize=15\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By contrast, models that introduce structure through priors—either by tightening the prior range on $\\rho$ or imposing shrinkage on the regression coefficients—perform noticeably better. The tight-$\\rho$ models regularize the latent correlation, effectively limiting the extent to which endogeneity can distort inference, while spike-and-slab and horseshoe priors perform selective shrinkage on the covariates, allowing the model to emphasize variables that genuinely predict the treatment. This helps isolate more valid “instrument-like” components of variation, pulling the posterior of $\\alpha$ closer to the true causal effect. \n",
+    "\n",
+    "The exclusion-restriction specification, which enforces prior beliefs about which covariates affect only the treatment or only the outcome, performs well too. The imposed restrictions recover both the correct treatment effect and a tight estimate of residual correlation. It may be wishful thinking that this precise instrument structure is available to an analyst in the applied setting, but instrument variable designs and their imposed exclusion restrictions should be motivated by theory. Where that theory is plausible we can hope for such precise estimates.\n",
+    "\n",
+    "Together, these results illustrate the power of Bayesian joint modelling: even in the presence of confounding, appropriate prior structure enables partial recovery of causal effects. Importantly, the priors do not simply “fix” the bias—they make explicit the trade-offs between flexibility and identification. This transparency is one of the key advantages of Bayesian causal inference over traditional reduced-form methods.\n",
+    "\n",
+    "We can see similar patterns in the below pair plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2, 3, figsize=(15, 8), sharex=True, sharey=True)\n",
+    "axs = axs.flatten()\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"spike_and_slab\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[0],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"horseshoe\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[1],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"exclusion\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[2],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"normal\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[3],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"rho_tight\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[4],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"rho_tight_spike_slab\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[5],\n",
+    ")\n",
+    "for ax, m in zip(\n",
+    "    axs,\n",
+    "    [\n",
+    "        \"spike_slab\",\n",
+    "        \"horse shoe\",\n",
+    "        \"exclusion_restriction\",\n",
+    "        \"normal\",\n",
+    "        \"tight_rho\",\n",
+    "        \"tight_rho_spike_slab\",\n",
+    "    ],\n",
+    "):\n",
+    "    ax.axvline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n",
+    "    ax.axhline(0.6, linestyle=\"--\", color=\"red\", label=\"True rho\")\n",
+    "    ax.set_title(f\"Posterior Relationship {m}\")\n",
+    "    ax.legend(loc=\"lower left\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each panel displays the joint posterior density between these two parameters for a given model specification.\n",
+    "\n",
+    "In the baseline normal model, the posteriors of $\\alpha$ and $\\rho$ exhibit a strong negative association: as the inferred residual correlation decreases, the estimated treatment effect increases. This pattern is characteristic of endogeneity. Part of the treatment’s apparent effect on the outcome is actually explained by unobserved factors that simultaneously drive both. The normal model correctly detects confounding but cannot disentangle its consequences without additional structure, leaving the treatment effect biased.\n",
+    "\n",
+    "One other feature evident from the spike and slab and horseshoe models is that the posterior distribution is somewhat bi-modal. The evidence pulls in two ways. There is not sufficient evidence in the data alone for the model to decisively characterise the $\\rho$ parameter and this induces a schizophrenic posterior distribution in the $\\alpha$ values estimated with these models. In other words, the posterior appears bi-modal. There are multiple centres of mass in the probability distribution representing a kind of indecision or oscillation between two views of the world.\n",
+    "\n",
+    "Introducing tight-$\\rho$ priors fundamentally changes this relationship. By constraining the allowable range of to moderate values, we effectively impose an analyst’s belief that the degree of confounding, while nonzero, is not overwhelming. This acts as a form of structural regularization: the posterior of $\\alpha$ stabilizes around the true causal effect. In practice, this mirrors what applied analysts often do implicitly. By imposing a weakly informative prior we anchor the model with plausible bounds on endogeneity rather than assuming perfect exogeneity or unbounded correlation. The preference for weakly informative priors here improves the sampling geometry but also clarifies the theoretical position of the analyst. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
rho_tightalpha3.0980.2372.6543.5110.0080.008949.0861.01.00
rho0.5080.1640.1980.8020.0050.003954.0750.01.00
normalalpha3.5600.4002.8414.3500.0180.014511.0522.01.01
rho0.0420.386-0.6950.6780.0170.008511.0563.01.01
spike_slabalpha3.3620.4912.5813.9840.0470.007139.01001.01.05
rho0.1930.460-0.3980.8120.0450.003138.0926.01.05
horseshoealpha3.4140.4052.7023.9890.0210.008427.01169.01.01
rho0.1690.391-0.3940.7840.0200.004427.01101.01.01
exclusion_restrictionalpha2.7640.1532.4643.0360.0050.0031168.01351.01.00
rho0.7180.0620.6110.8440.0020.0011181.01478.01.00
tight_rho_spike_slabalpha2.8290.2192.4463.2510.0070.005915.01419.01.00
rho0.6780.1070.4690.8510.0040.002923.01483.01.00
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                              mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
+       "rho_tight             alpha  3.098  0.237   2.654    3.511      0.008   \n",
+       "                      rho    0.508  0.164   0.198    0.802      0.005   \n",
+       "normal                alpha  3.560  0.400   2.841    4.350      0.018   \n",
+       "                      rho    0.042  0.386  -0.695    0.678      0.017   \n",
+       "spike_slab            alpha  3.362  0.491   2.581    3.984      0.047   \n",
+       "                      rho    0.193  0.460  -0.398    0.812      0.045   \n",
+       "horseshoe             alpha  3.414  0.405   2.702    3.989      0.021   \n",
+       "                      rho    0.169  0.391  -0.394    0.784      0.020   \n",
+       "exclusion_restriction alpha  2.764  0.153   2.464    3.036      0.005   \n",
+       "                      rho    0.718  0.062   0.611    0.844      0.002   \n",
+       "tight_rho_spike_slab  alpha  2.829  0.219   2.446    3.251      0.007   \n",
+       "                      rho    0.678  0.107   0.469    0.851      0.004   \n",
+       "\n",
+       "                             mcse_sd  ess_bulk  ess_tail  r_hat  \n",
+       "rho_tight             alpha    0.008     949.0     861.0   1.00  \n",
+       "                      rho      0.003     954.0     750.0   1.00  \n",
+       "normal                alpha    0.014     511.0     522.0   1.01  \n",
+       "                      rho      0.008     511.0     563.0   1.01  \n",
+       "spike_slab            alpha    0.007     139.0    1001.0   1.05  \n",
+       "                      rho      0.003     138.0     926.0   1.05  \n",
+       "horseshoe             alpha    0.008     427.0    1169.0   1.01  \n",
+       "                      rho      0.004     427.0    1101.0   1.01  \n",
+       "exclusion_restriction alpha    0.003    1168.0    1351.0   1.00  \n",
+       "                      rho      0.001    1181.0    1478.0   1.00  \n",
+       "tight_rho_spike_slab  alpha    0.005     915.0    1419.0   1.00  \n",
+       "                      rho      0.002     923.0    1483.0   1.00  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_params = pd.concat(\n",
+    "    {\n",
+    "        \"rho_tight\": az.summary(\n",
+    "            idata_confounded[\"rho_tight\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"normal\": az.summary(idata_confounded[\"normal\"], var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"spike_slab\": az.summary(\n",
+    "            idata_confounded[\"spike_and_slab\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"horseshoe\": az.summary(\n",
+    "            idata_confounded[\"horseshoe\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"exclusion_restriction\": az.summary(\n",
+    "            idata_confounded[\"exclusion\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"tight_rho_spike_slab\": az.summary(\n",
+    "            idata_confounded[\"rho_tight_spike_slab\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "df_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Across all specifications, the diagnostics tell a consistent story: effective sample sizes are high, `rhat` values hover near 1.00, and divergent transitions are minimal or absent. These are healthy traces, suggesting that the posterior geometries are well-explored and that the models are numerically stable under their respective prior assumptions.\n",
+    "\n",
+    "### Causal Identification and Variable Selection\n",
+    "\n",
+    "Before continuing to the binary case it's worth diving into the role of priors in these structural causal models. Both spike and slab and horseshoe priors were designed to perform automatic variable selection. The spike-and-slab via a latent mixture of near-zero and freely estimated components, and the horseshoe through continuous shrinkage that allows strong predictors to survive while damping weak or spurious ones. Ultimately these priors determine the multiplicative weights of the $\\beta$ coefficients in the model. By placing these variable selection priors on the weights, they are calibrated against the data so as to zero-out those variables that are not required. For a more thorough discussion of automated variable selection using priors we recommend {cite:t}`kaplan_bs_social_science` and the [pymc discourse](https://discourse.pymc.io/t/question-on-how-to-model-spike-and-slab-priors/5277) site.\n",
+    "\n",
+    "Plotting these posteriors vividly illustrates their behavior more clearly than describing it. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(\n",
+    "    idata_confounded[\"rho_tight_spike_slab\"], var_names=[\"beta_O\"], figsize=(20, 10)\n",
+    ")\n",
+    "plt.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This form of Bayesian regularization is crucial when the analyst suspects structural bias i.e. when some covariates may themselves be noise. By letting the model discover and downweight such variables, these priors act as a safeguard against overfitting endogenous structure. Bayesian variable selection is not merely a statistical convenience, but a structural choice about what relationships should be allowed to persist in the causal model. But this behavior should not be mistaken for a _magical salve_ for endogeneity. No prior, however clever, can know which variables are truly exogenous or which exclusion restrictions are defensible. That judgment must come from theory, domain expertise, and a careful causal design. \n",
+    "\n",
+    "Seen this way, these priors are best thought of as complements to theory, not substitutes for it. They are powerful tools for regularization and for exploring the robustness of our inferences, especially in high-dimensional or structurally ambiguous settings. Yet, they should always be deployed with a clear rationale about what the analyst believes to be the relevant sources of variation—and why."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Binary Treatment Case\n",
+    "\n",
+    "In practice, theory-driven variable selection tends to be more tractable when the treatment is binary. For instance, when a treatment represents a policy adoption, a clinical intervention, or a discrete decision - like entering a program or not. In such settings, the causal question is easier to articulate in design terms: What would have happened if this unit had not received the treatment? Because the intervention is categorical, analysts can often draw on institutional knowledge or policy mechanisms to reason about which variables are genuine confounders, which might serve as instruments, and which can be safely excluded. This clarity of design focus makes the binary treatment context an ideal laboratory for contrasting structural Bayesian modeling with the potential outcomes perspective.\n",
+    "\n",
+    "This also allows us to explore how Bayesian joint modeling connects to the potential outcomes framework, where causal effects are conceptualized not just as slopes in a regression, but as differences in counterfactual predictions.To explore this, we adapt our earlier joint modeling setup to the binary treatment context. The model below replaces the continuous treatment equation with a latent variable formulation that links predictors to a Bernoulli decision through a logistic transformation. The latent variables $U$ and $V$ introduce correlated residuals between the outcome and treatment equations, controlled by a correlation parameter $\\rho$. This setup captures endogenous selection into the treatment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "cluster2500 x 9\n",
+       "\n",
+       "2500 x 9\n",
+       "\n",
+       "\n",
+       "cluster2500\n",
+       "\n",
+       "2500\n",
+       "\n",
+       "\n",
+       "cluster2500 x 2\n",
+       "\n",
+       "2500 x 2\n",
+       "\n",
+       "\n",
+       "clusterbeta_treatment (9)\n",
+       "\n",
+       "beta_treatment (9)\n",
+       "\n",
+       "\n",
+       "clusterbeta_outcome (9)\n",
+       "\n",
+       "beta_outcome (9)\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data\n",
+       "\n",
+       "X_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_outcome\n",
+       "\n",
+       "mu_outcome\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_treatment\n",
+       "\n",
+       "mu_treatment\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_treatment\n",
+       "\n",
+       "likelihood_treatment\n",
+       "~\n",
+       "Bernoulli\n",
+       "\n",
+       "\n",
+       "\n",
+       "t_data\n",
+       "\n",
+       "t_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_treatment->t_data\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_outcome\n",
+       "\n",
+       "likelihood_outcome\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_outcome->likelihood_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_treatment->likelihood_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "t_data->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "y_data\n",
+       "\n",
+       "y_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_outcome->y_data\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho_unconstr\n",
+       "\n",
+       "rho_unconstr\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho\n",
+       "\n",
+       "rho\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho_unconstr->rho\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "alpha\n",
+       "\n",
+       "alpha\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "alpha->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U\n",
+       "\n",
+       "sigma_U\n",
+       "~\n",
+       "Halfnormal\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U->likelihood_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps\n",
+       "\n",
+       "eps\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps_raw\n",
+       "\n",
+       "eps_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps_raw->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_T\n",
+       "\n",
+       "beta_T\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_T->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_O\n",
+       "\n",
+       "beta_O\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_O->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_confounded = simulate_data(n=2500, alpha_true=3, rho=0.6, cate_estimation=True)\n",
+    "\n",
+    "\n",
+    "coords = {\n",
+    "    \"beta_outcome\": [col for col in data_unconfounded.columns if \"feature\" in col],\n",
+    "    \"beta_treatment\": [col for col in data_unconfounded.columns if \"feature\" in col],\n",
+    "    \"obs\": range(data_unconfounded.shape[0]),\n",
+    "    \"latent\": [\"U\", \"V\"],\n",
+    "    \"sigmas_1\": [\"var_U\", \"cov_UV\"],\n",
+    "    \"sigmas_2\": [\"cov_VU\", \"var_V\"],\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def make_binary_model(\n",
+    "    data,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    bart_outcome=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=None,\n",
+    "    Y=None,\n",
+    "    T=None,\n",
+    "    priors=None,\n",
+    "    observed=True,\n",
+    "    spike_and_slab=False,\n",
+    "):\n",
+    "    if X is None:\n",
+    "        X = data[[col for col in data.columns if \"feature\" in col]]\n",
+    "        Y = data[\"Y_bin\"].values\n",
+    "        T = data[\"T_bin\"].values\n",
+    "\n",
+    "    if priors is None:\n",
+    "        priors = {\n",
+    "            \"rho\": [0, 0.5],\n",
+    "            \"alpha\": [0, 10],\n",
+    "            \"beta_O\": [0, 1],\n",
+    "            \"eps\": [0, 1],\n",
+    "            \"sigma_U\": [1],\n",
+    "        }\n",
+    "\n",
+    "    with pm.Model(coords=coords) as binary_model:\n",
+    "        X_data = pm.Data(\"X_data\", X.values)\n",
+    "        y_data = pm.Data(\"y_data\", Y)\n",
+    "        t_data = pm.Data(\"t_data\", T)\n",
+    "\n",
+    "        alpha = pm.Normal(\"alpha\", priors[\"alpha\"][0], priors[\"alpha\"][1])\n",
+    "        sigma_U = pm.HalfNormal(\"sigma_U\", priors[\"sigma_U\"][0])\n",
+    "        # just correlation, not full covariance\n",
+    "\n",
+    "        rho_unconstr = pm.Normal(\"rho_unconstr\", priors[\"rho\"][0], priors[\"rho\"][1])\n",
+    "        rho = pm.Deterministic(\"rho\", pm.math.tanh(rho_unconstr))  # keep |rho|<1\n",
+    "\n",
+    "        inverse_rho = pm.math.sqrt(pm.math.maximum(1 - rho**2, 1e-12))\n",
+    "        chol = pt.stack([[sigma_U, 0.0], [sigma_U * rho, inverse_rho]])\n",
+    "\n",
+    "        # --- Draw latent errors ---\n",
+    "        eps_raw = pm.Normal(\n",
+    "            \"eps_raw\", priors[\"eps\"][0], priors[\"eps\"][1], shape=(len(data), 2)\n",
+    "        )\n",
+    "        eps = pm.Deterministic(\"eps\", pt.dot(eps_raw, chol.T))\n",
+    "\n",
+    "        U = eps[:, 0]\n",
+    "        V = eps[:, 1]\n",
+    "\n",
+    "        if bart_treatment:\n",
+    "            mu_treatment = pmb.BART(\"mu_treatment_bart\", X=X_data, Y=t_data) + V\n",
+    "        else:\n",
+    "            beta_treatment = pm.Normal(\"beta_T\", 0, 1, dims=\"beta_treatment\")\n",
+    "            mu_treatment = pm.Deterministic(\n",
+    "                \"mu_treatment\", (X_data @ beta_treatment) + V\n",
+    "            )\n",
+    "        p_t = pm.math.invlogit(mu_treatment)\n",
+    "        if observed:\n",
+    "            _ = pm.Bernoulli(\"likelihood_treatment\", p_t, observed=t_data)\n",
+    "        else:\n",
+    "            _ = pm.Bernoulli(\"likelihood_treatment\", p_t)\n",
+    "\n",
+    "        if cate_estimation:\n",
+    "            pi_O = pm.Beta(\"pi_O\", alpha=2, beta=2)\n",
+    "            alpha_O_raw = pm.Normal(\"alpha_O_raw\", mu=0, sigma=2, dims=\"beta_outcome\")\n",
+    "            gamma_O = relaxed_bernoulli(\n",
+    "                \"gamma_O\", pi_O, temperature=0.1, dims=\"beta_outcome\"\n",
+    "            )\n",
+    "            alpha_interaction_outcome = pm.Deterministic(\n",
+    "                \"alpha_interact\", gamma_O * alpha_O_raw, dims=\"beta_outcome\"\n",
+    "            )\n",
+    "            alpha = alpha + pm.math.dot(X_data, alpha_interaction_outcome)\n",
+    "\n",
+    "        if bart_outcome:\n",
+    "            mu_outcome = pmb.BART(\"mu_outcome_bart\", X=X_data, Y=y_data) + U\n",
+    "        else:\n",
+    "            if spike_and_slab:\n",
+    "                pi_O = pm.Beta(\"pi_O_b\", alpha=2, beta=2)\n",
+    "                beta_O_raw = pm.Normal(\"beta_O_raw\", mu=0, sigma=2, dims=\"beta_outcome\")\n",
+    "                gamma_O = relaxed_bernoulli(\n",
+    "                    \"gamma_O_b\", pi_O, temperature=0.1, dims=\"beta_outcome\"\n",
+    "                )\n",
+    "                beta_outcome = pm.Deterministic(\"beta_O\", gamma_O * beta_O_raw)\n",
+    "                mu_outcome = pm.Deterministic(\n",
+    "                    \"mu_outcome\", (X_data @ beta_outcome) + alpha * t_data + U\n",
+    "                )\n",
+    "            else:\n",
+    "                beta_outcome = pm.Normal(\n",
+    "                    \"beta_O\",\n",
+    "                    priors[\"beta_O\"][0],\n",
+    "                    priors[\"beta_O\"][1],\n",
+    "                    dims=\"beta_outcome\",\n",
+    "                )\n",
+    "                mu_outcome = pm.Deterministic(\n",
+    "                    \"mu_outcome\", (X_data @ beta_outcome) + alpha * t_data + U\n",
+    "                )\n",
+    "\n",
+    "        if observed:\n",
+    "            _ = pm.Normal(\n",
+    "                \"likelihood_outcome\", mu_outcome, sigma=sigma_U, observed=y_data\n",
+    "            )\n",
+    "        else:\n",
+    "            _ = pm.Normal(\"likelihood_outcome\", mu_outcome, sigma=sigma_U)\n",
+    "\n",
+    "    return binary_model\n",
+    "\n",
+    "\n",
+    "binary_model_bart_treatment = make_binary_model(\n",
+    "    data_confounded, coords, bart_treatment=True\n",
+    ")\n",
+    "binary_model_bart_treatment_cate = make_binary_model(\n",
+    "    data_confounded, coords, bart_treatment=True, cate_estimation=True\n",
+    ")\n",
+    "binary_model = make_binary_model(data_confounded, coords)\n",
+    "binary_model_bart_outcome = make_binary_model(\n",
+    "    data_confounded, coords, bart_outcome=True\n",
+    ")\n",
+    "pm.model_to_graphviz(binary_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The nested dependency structure of the model can be seen clearly in the graph above. In the binary setting, the, $\\alpha$ parameter captures the average difference in outcomes between treated and untreated units, but as before we are aiming to capture a treatment effect estimate of 3. This model is still bivariate normal in that the latent draws of `eps_raw` are transformed to reflect the correlation encoded in $\\rho$. \n",
+    "\n",
+    "$$\n",
+    "\\epsilon_{\\text{raw}, i} =\n",
+    "\\begin{pmatrix} \\epsilon_{U,i}^{\\text{raw}} \\ \\epsilon_{V,i}^{\\text{raw}} \\end{pmatrix}\n",
+    "\\sim \\mathcal{N}\\left(\\begin{pmatrix} 0 \\ 0 \\end{pmatrix}, \\mathbf{I}_2\\right)\n",
+    "$$\n",
+    "\n",
+    "due to the dot product multiplication\n",
+    "\n",
+    "$$\n",
+    "\n",
+    "\\begin{pmatrix} U_i \\ V_i \\end{pmatrix} = \\mathbf{chol} \\cdot \\epsilon_{\\text{raw}, i} \\sim \\mathcal{N}\\left(\n",
+    "\\begin{pmatrix} 0 \\ 0 \\end{pmatrix},\n",
+    "\\mathbf{\\Sigma} \\right)\n",
+    "\n",
+    "$$\n",
+    "\n",
+    "This is a convenient representation for the bivariate binary case that samples quite efficiently. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, eps_raw, likelihood_outcome, likelihood_treatment, mu_treatment_bart, rho_unconstr, sigma_U]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_O]\n",
+      ">PGBART: [mu_treatment_bart]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3b7750b94e94ce6a4a7141dab77beac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 101 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46d346cf19c04a159b8b3a766431c825",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 29 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [alpha, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, mu_outcome_bart, rho_unconstr, sigma_U]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T]\n",
+      ">PGBART: [mu_outcome_bart]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "646f2a51937a443abb1db22493393bb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 98 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [alpha, alpha_O_raw, beta_O, eps_raw, gamma_O_u, likelihood_outcome, likelihood_treatment, mu_treatment_bart, pi_O, rho_unconstr, sigma_U]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, pi_O, alpha_O_raw, gamma_O_u, beta_O]\n",
+      ">PGBART: [mu_treatment_bart]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "768dcc2860c94f7aabbe09939cd10c78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 241 seconds.\n",
+      "There were 31 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fit_binary_model(model):\n",
+    "    with model:\n",
+    "        idata = pm.sample_prior_predictive()\n",
+    "        idata.extend(pm.sample(target_accept=0.95))\n",
+    "    return idata\n",
+    "\n",
+    "\n",
+    "idata_binary_model_bart_treatment = fit_binary_model(binary_model_bart_treatment)\n",
+    "idata_binary_model = fit_binary_model(binary_model)\n",
+    "idata_binary_bart_outcome = fit_binary_model(binary_model_bart_outcome)\n",
+    "idata_binary_bart_treatment_cate = fit_binary_model(binary_model_bart_treatment_cate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Comparing Treatment Estimates\n",
+    "\n",
+    "Three of our four approaches successfully recover the true causal effect of 3.0, with tight uncertainty bands and accurate confounding estimates. But when BART enters the outcome equation, the results collapse: the treatment effect estimate drops to near-zero. This is not a sampling failure. Diagnostics show healthy chains, good ESS, and converged r-hat values. The model is doing exactly what we asked it to do. The problem is what we asked the model to do!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2, 2, figsize=(20, 6), sharex=True)\n",
+    "axs = axs.flatten()\n",
+    "az.plot_posterior(idata_binary_model_bart_treatment, var_names=\"alpha\", ax=axs[0])\n",
+    "az.plot_posterior(idata_binary_bart_outcome, var_names=\"alpha\", ax=axs[1])\n",
+    "az.plot_posterior(idata_binary_model, var_names=\"alpha\", ax=axs[2])\n",
+    "az.plot_posterior(idata_binary_bart_treatment_cate, var_names=\"alpha\", ax=axs[3])\n",
+    "for ax, title in zip(\n",
+    "    axs, [\"bart_treatment\", \"bart_outcome\", \"no_bart\", \"bart_treatment_cate\"]\n",
+    "):\n",
+    "    ax.axvline(3, linestyle=\"--\", color=\"k\")\n",
+    "    ax.set_title(f\"Model: {title}\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The failure stems from a fundamental tension between flexibility and causal identification. In our data generating process the treatment is strongly predicted by the covariates. The flexibility of the BART outcome model picks up on this pattern. It learns the total association and does not distinguish causal relationships from association. When we then add a structural parameter α for the treatment effect, we're asking: what is the effect of the treatment _after_ BART has already explained outcome variation using the treatment predictive features. We can see this reflected in the $\\rho$ parameter for the BART outcome model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    \n",
+       "  \n",
+       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
linear_no_bartalpha3.5230.1243.2893.7490.0060.002454.01068.01.01
rho0.5420.0570.4310.6450.0030.001379.0827.01.01
bart_treatmentalpha3.5610.1253.3373.8060.0060.003445.0997.01.02
rho0.5200.0540.4220.6290.0030.001367.0826.01.02
bart_outcomealpha0.00710.257-20.13818.6110.1340.1745901.02583.01.00
rho0.9740.0110.9540.9920.0000.0003294.02713.01.00
bart_treatment_catealpha3.2300.1133.0263.4500.0050.002586.01245.01.00
rho0.7460.0620.6350.8660.0030.002426.0698.01.01
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                            mean      sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "linear_no_bart      alpha  3.523   0.124   3.289    3.749      0.006    0.002   \n",
+       "                    rho    0.542   0.057   0.431    0.645      0.003    0.001   \n",
+       "bart_treatment      alpha  3.561   0.125   3.337    3.806      0.006    0.003   \n",
+       "                    rho    0.520   0.054   0.422    0.629      0.003    0.001   \n",
+       "bart_outcome        alpha  0.007  10.257 -20.138   18.611      0.134    0.174   \n",
+       "                    rho    0.974   0.011   0.954    0.992      0.000    0.000   \n",
+       "bart_treatment_cate alpha  3.230   0.113   3.026    3.450      0.005    0.002   \n",
+       "                    rho    0.746   0.062   0.635    0.866      0.003    0.002   \n",
+       "\n",
+       "                           ess_bulk  ess_tail  r_hat  \n",
+       "linear_no_bart      alpha     454.0    1068.0   1.01  \n",
+       "                    rho       379.0     827.0   1.01  \n",
+       "bart_treatment      alpha     445.0     997.0   1.02  \n",
+       "                    rho       367.0     826.0   1.02  \n",
+       "bart_outcome        alpha    5901.0    2583.0   1.00  \n",
+       "                    rho      3294.0    2713.0   1.00  \n",
+       "bart_treatment_cate alpha     586.0    1245.0   1.00  \n",
+       "                    rho       426.0     698.0   1.01  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat(\n",
+    "    {\n",
+    "        \"linear_no_bart\": az.summary(idata_binary_model, var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"bart_treatment\": az.summary(\n",
+    "            idata_binary_model_bart_treatment, var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"bart_outcome\": az.summary(\n",
+    "            idata_binary_bart_outcome, var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"bart_treatment_cate\": az.summary(\n",
+    "            idata_binary_bart_treatment_cate, var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `bart_outcome` model places weight on the correlation between treatment and outcome rather than parcel out the share of impact into the treatment and confounding relationship. The causal effect absorbed into the covariate adjustment of the BART component, and we have a fundamental misattribution which makes recovery of structural parameter impossible in this set up. The other two BART model specifications; `bart_treatment` and `bart_treatment_cate` correctly identify the structural parameter because the BART component is used to flexibly model the treatment status. The structural parameter $\\alpha$ remains identifiable as the average or baseline effect because we've partialied out the variation in the outcome explicitly. The more traditional `linear_no_bart` model does not have the flexibility to absorb the causal effect into a non-linear component. As such, the structural parameter remains identifiable. This is one of the virtues of \"simpler\" models. \n",
+    "\n",
+    "### Non-Parametric Causal Inference\n",
+    "\n",
+    "We might worry that these parametric approaches to identifying causal effects hide the real lesson. Non-parametric approximation functions can still learn the correct expected value function and we ought to derive causal estimates via the imputation of potential outcomes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](../_static/probabilistic_intervention_fix.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should verify that the BART-outcome model's failure isn't merely a problem with how we've extracted the treatment effect parameter $\\alpha$. Perhaps the structural parameter collapsed, but the model could still recover causal effects through direct counterfactual imputation. Rather than interpreting a regression coefficient, we directly simulate potential outcomes:\n",
+    "\n",
+    "- Fit a model for $E[Y | X, T]$ (however flexible)\n",
+    "- Impute $Y(1)$: Set everyone to treated, predict outcomes\n",
+    "- Impute $Y(0)$: Set everyone to control, predict outcomes\n",
+    "- Compute ATE: Average the difference $Y(1) - Y(0)$\n",
+    "\n",
+    "This approach is appealing because it doesn't require interpreting structural parameters. If the model has learned the correct conditional expectation function, counterfactual imputation should recover the true causal effect—even if $\\alpha$ itself is uninterpretable. This process of imputation is then repeated across many, many samples to derive the posterior distribution of the treatment effect. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4570bceee324ecabf2fa440103a6c63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
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+       "Output()"
+      ]
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+    {
+     "data": {
+      "text/html": [
+       "
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+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
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+     "metadata": {},
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imputed Difference in Potential Outcomes 3.563278983428259\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
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+    {
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
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+    {
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+       "Output()"
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+     "output_type": "display_data"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imputed Difference in Potential Outcomes 0.0003075874678246132\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
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+    {
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+       "Output()"
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
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+    {
+     "data": {
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+       "Output()"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imputed Difference in Potential Outcomes 3.524198759477424\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68c51c4a994f419293febe242949d5bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imputed Difference in Potential Outcomes 3.230870608602349\n"
+     ]
+    }
+   ],
+   "source": [
+    "def impute_potential_outcomes(model, idata, n=2500):\n",
+    "    with model:\n",
+    "        # Posterior predictive under treatment\n",
+    "        pm.set_data({\"t_data\": np.ones(n, dtype=\"int\")})\n",
+    "        Y1 = pm.sample_posterior_predictive(idata, var_names=[\"likelihood_outcome\"])\n",
+    "\n",
+    "        # Posterior predictive under control\n",
+    "        pm.set_data({\"t_data\": np.zeros(n, dtype=\"int\")})\n",
+    "        Y0 = pm.sample_posterior_predictive(idata, var_names=[\"likelihood_outcome\"])\n",
+    "        ATE = (\n",
+    "            Y1[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    "            - Y0[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    "        ).mean()\n",
+    "        print(\"Imputed Difference in Potential Outcomes\", ATE.item())\n",
+    "    return Y1, Y0, ATE.item()\n",
+    "\n",
+    "\n",
+    "y1_bart_treatment, y0_bart_treatment, ate_bart_treatment = impute_potential_outcomes(\n",
+    "    binary_model_bart_treatment, idata_binary_model_bart_treatment\n",
+    ")\n",
+    "\n",
+    "y1_bart_outcome, y0_bart_outcome, ate_bart_outcome = impute_potential_outcomes(\n",
+    "    binary_model_bart_outcome, idata_binary_bart_outcome\n",
+    ")\n",
+    "\n",
+    "y1_no_bart, y0_no_bart, ate_linear = impute_potential_outcomes(\n",
+    "    binary_model, idata_binary_model\n",
+    ")\n",
+    "\n",
+    "y1_treatment_cate, y0_treatment_cate, ate_cate = impute_potential_outcomes(\n",
+    "    binary_model_bart_treatment_cate, idata_binary_bart_treatment_cate\n",
+    ")\n",
+    "\n",
+    "imputed_effects = pd.DataFrame(\n",
+    "    {\n",
+    "        \"model\": [\n",
+    "            \"bart_treatment\",\n",
+    "            \"bart_outcome\",\n",
+    "            \"linear_no_bart\",\n",
+    "            \"bart_treatment_cate\",\n",
+    "        ],\n",
+    "        \"ate\": [ate_bart_treatment, ate_bart_outcome, ate_linear, ate_cate],\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above code we have applied the following process to impute potential outcomes for each individual under different treatment regimes. \n",
+    "\n",
+    "![](../_static/potential_outcomes.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are striking in their consistency. For the three successful specifications, both methods of extracting causal effects agree. For the `bart_outcome` specification the Imputation approach to causal inference also fails. This is crucial. The failure is not about how we interrogate the model, but about what the model learned during fitting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
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+       "  \n",
+       "
modelate
0bart_treatment3.563279
1bart_outcome0.000308
2linear_no_bart3.524199
3bart_treatment_cate3.230871
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                 model       ate\n",
+       "0       bart_treatment  3.563279\n",
+       "1         bart_outcome  0.000308\n",
+       "2       linear_no_bart  3.524199\n",
+       "3  bart_treatment_cate  3.230871"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imputed_effects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In prediction tasks, BART's flexibility is a pure advantage. It finds patterns we didn't know to look for, captures complex interactions automatically, and often achieves superior out-of-sample accuracy. But in causal inference, this same flexibility becomes a liability when it absorbs the variation we're trying to causally attribute. The problem is **structural**: any sufficiently flexible method faces this challenge. Methods that can perfectly adapt their functional form to training data will inadvertently learn causal pathways as associational patterns, unless the structure learning is constrained to partial out the treatment influences. The stronger the relationship between the predictors of the outcome and the treatment, the more we can expect to see this collapse. Flexible outcome modelling may be useful in cases where the relationship between treatment and covariates is truly independent, but it presents a risk where the focus is on recovering treatment effects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Conditional Average Treatment Effects\n",
+    "\n",
+    "The BART-treatment model demonstrated that flexibility in the treatment equation doesn't harm identification. We can also introduce flexibility in how treatment effects vary with covariates, while preserving the interpretability and identifiability of structural parameters? Our `bart_treatment_cate` model allows this by interacting the treatment parameter with the covariates. This explicitly parameterize effect heterogeneity. Unlike BART in the outcome equation (which failed because it absorbed the entire treatment signal), interaction terms allow treatment effects to vary while retaining a structural interpretation. This allows flexibility while retaining identifiability. \n",
+    "\n",
+    "We can see this flexibility by pulling out the ITE (individual treatment effects) estimates, using the potential outcomes imputations. We can compare the ITEs across the `bart_treatment_cate` and `linear_no_bart` models. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cate = (\n",
+    "    y1_treatment_cate[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    "    - y0_treatment_cate[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "sample_cate = (\n",
+    "    cate.mean(dim=(\"chain\", \"draw\"))\n",
+    "    .to_dataframe()\n",
+    "    .sample(100)\n",
+    "    .sort_values(\"likelihood_outcome\")\n",
+    ")\n",
+    "\n",
+    "cate_linear = (\n",
+    "    y1_no_bart[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    "    - y0_no_bart[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    ")\n",
+    "\n",
+    "res = smf.ols(\n",
+    "    \"\"\"Y_bin ~ T_bin + feature_0 + feature_1 + feature_2 + feature_3 + feature_4 + feature_5 + feature_6 + feature_7 + feature_8\"\"\",\n",
+    "    data,\n",
+    ").fit()\n",
+    "ols_est = res.params[\"T_bin\"]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(15, 20))\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "ax = az.plot_forest(\n",
+    "    cate_linear,\n",
+    "    combined=True,\n",
+    "    figsize=(15, 15),\n",
+    "    coords={\"likelihood_outcome_dim_0\": sample_cate.index},\n",
+    "    ax=axs[0],\n",
+    ")\n",
+    "axs[0].axvline(3, color=\"red\", label=\"True Treatment Effect\")\n",
+    "axs[0].axvline(ols_est, color=\"darkgreen\", label=\"OLS Estimate\")\n",
+    "axs[0].legend()\n",
+    "axs[0].set_title(\"ITE Linear-Model estimates \\n Random Sample of 100\")\n",
+    "\n",
+    "ax = az.plot_forest(\n",
+    "    cate,\n",
+    "    combined=True,\n",
+    "    figsize=(15, 10),\n",
+    "    coords={\"likelihood_outcome_dim_0\": sample_cate.index},\n",
+    "    ax=axs[1],\n",
+    ")\n",
+    "axs[1].axvline(3, color=\"red\", label=\"True Treatment Effect\")\n",
+    "axs[1].axvline(ols_est, color=\"darkgreen\", label=\"OLS Estimate\")\n",
+    "axs[1].set_title(\"ITE CATE-Model estimates \\n Random Sample of 100\")\n",
+    "\n",
+    "n = len(sample_cate.index)\n",
+    "n_order = range(n, 0, -1)\n",
+    "axs[1].scatter(data_confounded.iloc[sample_cate.index][\"alpha\"], n_order, color=\"black\")\n",
+    "axs[1].legend()\n",
+    "plt.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This comparison shows how the flexibility of treatment effects can be incorporated without losing interpretability. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### An Empirical Application\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now explore an example with a real data set from the NHEFS study about the effects of quitting smoking on weight. Ultimately we will compare our estimates of treatment effects to a well specified and sensible regression model. The goal is not to provide the regression estimate as a source of truth, but to show the reasonable diversity of views we can achieve while aiming to estimate causal impact. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "cluster1566 x 14\n",
+       "\n",
+       "1566 x 14\n",
+       "\n",
+       "\n",
+       "cluster1566\n",
+       "\n",
+       "1566\n",
+       "\n",
+       "\n",
+       "cluster1566 x 2\n",
+       "\n",
+       "1566 x 2\n",
+       "\n",
+       "\n",
+       "clusterbeta_treatment (14)\n",
+       "\n",
+       "beta_treatment (14)\n",
+       "\n",
+       "\n",
+       "clusterbeta_outcome (14)\n",
+       "\n",
+       "beta_outcome (14)\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data\n",
+       "\n",
+       "X_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_outcome\n",
+       "\n",
+       "mu_outcome\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_treatment\n",
+       "\n",
+       "mu_treatment\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_treatment\n",
+       "\n",
+       "likelihood_treatment\n",
+       "~\n",
+       "Bernoulli\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_outcome\n",
+       "\n",
+       "likelihood_outcome\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_outcome->likelihood_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_treatment->likelihood_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "t_data\n",
+       "\n",
+       "t_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "t_data->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "y_data\n",
+       "\n",
+       "y_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho_unconstr\n",
+       "\n",
+       "rho_unconstr\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho\n",
+       "\n",
+       "rho\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho_unconstr->rho\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "alpha\n",
+       "\n",
+       "alpha\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "alpha->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U\n",
+       "\n",
+       "sigma_U\n",
+       "~\n",
+       "Halfnormal\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U->likelihood_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps\n",
+       "\n",
+       "eps\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps_raw\n",
+       "\n",
+       "eps_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps_raw->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_T\n",
+       "\n",
+       "beta_T\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_T->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_O\n",
+       "\n",
+       "beta_O\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_O->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import causalpy as cp\n",
+    "\n",
+    "df_nhefs = cp.load_data(\"nhefs\")\n",
+    "\n",
+    "features = [\n",
+    "    \"age\",\n",
+    "    \"race\",\n",
+    "    \"sex\",\n",
+    "    \"smokeintensity\",\n",
+    "    \"smokeyrs\",\n",
+    "    \"wt71\",\n",
+    "    \"active_1\",\n",
+    "    \"active_2\",\n",
+    "    \"education_2\",\n",
+    "    \"education_3\",\n",
+    "    \"education_4\",\n",
+    "    \"education_5\",\n",
+    "    \"exercise_1\",\n",
+    "    \"exercise_2\",\n",
+    "]\n",
+    "X = df_nhefs[features]\n",
+    "X = (X - X.mean(axis=0)) / X.std(axis=0)\n",
+    "Y = df_nhefs[\"outcome\"].values\n",
+    "T = df_nhefs[\"trt\"].values\n",
+    "\n",
+    "\n",
+    "coords = {\n",
+    "    \"beta_outcome\": features,\n",
+    "    \"beta_treatment\": features,\n",
+    "    \"obs\": range(df_nhefs.shape[0]),\n",
+    "    \"latent\": [\"U\", \"V\"],\n",
+    "    \"sigmas_1\": [\"var_U\", \"cov_UV\"],\n",
+    "    \"sigmas_2\": [\"cov_VU\", \"var_V\"],\n",
+    "}\n",
+    "\n",
+    "priors = {\n",
+    "    \"rho\": [0.0, 0.5],\n",
+    "    \"alpha\": [0, 3],\n",
+    "    \"beta_O\": [0, 3],\n",
+    "    \"eps\": [0, 1],\n",
+    "    \"sigma_U\": [0.5],\n",
+    "}\n",
+    "\n",
+    "nhefs_binary_model = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors,\n",
+    "    observed=False,\n",
+    ")\n",
+    "pm.model_to_graphviz(nhefs_binary_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model is specified without the observed outcomes deliberately. We feed in the predictor $X$ and now we validate how the model specification can recover accurate treatment effects.\n",
+    "\n",
+    "#### Parameter Recovery\n",
+    "\n",
+    "We \"forward\" sample from the system with known parameters. This generates a synthetic observation data that we will feed back into the model, to condition on data known to have been sampled from this model. This makes use of PyMC's do-syntax. We are intervening on the data generating process to set values of the parameters in the system. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n"
+     ]
+    }
+   ],
+   "source": [
+    "fixed_parameters = {\n",
+    "    \"rho\": 0.6,\n",
+    "    \"alpha\": 3,\n",
+    "    \"beta_O\": [0, 1, 0.4, 0.3, 0.1, 0.8, 0, 0, 0, 0, 0, 0, 3, 0],\n",
+    "    \"beta_T\": [1, 1.3, 0.5, 0.3, 0.7, 1.6, 0, 0.4, 0, 0, 0, 0, 0, 0],\n",
+    "}\n",
+    "with pm.do(nhefs_binary_model, fixed_parameters) as synthetic_model:\n",
+    "    idata = pm.sample_prior_predictive(\n",
+    "        random_seed=1000\n",
+    "    )  # Sample from prior predictive distribution.\n",
+    "    synthetic_y = idata[\"prior\"][\"likelihood_outcome\"].sel(draw=0, chain=0)\n",
+    "    synthetic_t = idata[\"prior\"][\"likelihood_treatment\"].sel(draw=0, chain=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now infer the probable parameters conditioned on the synthetic observed dats. That is, we condition our model on the data generated in our forward pass and attempt the backwards inference. Given the synthetic observations what is the most plausible parameterisation of the world-state that generated the data?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1267690bf62d41898e5d6b5f56e31b23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 500 draw iterations (8_000 + 2_000 draws total) took 13 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Infer parameters conditioned on observed data\n",
+    "with pm.observe(\n",
+    "    nhefs_binary_model,\n",
+    "    {\"likelihood_outcome\": synthetic_y, \"likelihood_treatment\": synthetic_t},\n",
+    ") as inference_model:\n",
+    "    idata_sim = pm.sample_prior_predictive()\n",
+    "    idata_sim.extend(pm.sample(random_seed=100, chains=4, tune=2000, draws=500))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The inferential move allows us to accurately recover the focal parameters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = az.plot_pair(idata_sim, var_names=[\"alpha\", \"rho\"], kind=\"kde\", figsize=(15, 4))\n",
+    "ax.axhline(0.6, linestyle=\"--\", color=\"red\", label=\"True Rho\")\n",
+    "ax.axvline(3, linestyle=\"--\", color=\"black\", label=\"True Alpha\")\n",
+    "ax.set_title(\"Parameter Recovery with NHEFS data\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameter recovery is also extended to the covariate weights in the system. This is promising. It suggests that our model is able to recover true parameters from the data. Recovering covariate weights validates that the model correctly decomposes variation across the entire causal system, not just the focal treatment parameter.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(idata_sim, var_names=[\"beta_O\"], ref_val=fixed_parameters[\"beta_O\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Conditional Update on Observed Data\n",
+    "\n",
+    "Next we will condition on the actual observed data and apply a range of priors to the $\\rho$ term to test the sensitivity of our findings to prior weights. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf7284c4c9754817b57d7d120ba2f2fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 39 seconds.\n",
+      "Sampling: [alpha, alpha_O_raw, beta_O, eps_raw, gamma_O_u, likelihood_outcome, likelihood_treatment, mu_treatment_bart, pi_O, rho_unconstr, sigma_U]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, pi_O, alpha_O_raw, gamma_O_u, beta_O]\n",
+      ">PGBART: [mu_treatment_bart]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbf12d9a294448a38ed80a3ce03bf8e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 179 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c6c73eb8f1b4d70a2662928df012d12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 38 seconds.\n",
+      "Sampling: [alpha, beta_O_raw, beta_T, eps_raw, gamma_O_b_u, likelihood_outcome, likelihood_treatment, pi_O_b, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, pi_O_b, beta_O_raw, gamma_O_b_u]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae9aded74fe14280b95f300b05d45749",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
+      "  return 0.5 * np.dot(x, v_out)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
+      "  return 0.5 * np.dot(x, v_out)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 174 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "sampler_kwargs = {\n",
+    "    \"tune\": 2000,\n",
+    "    \"draws\": 1000,\n",
+    "    \"target_accept\": 0.95,\n",
+    "    \"mp_ctx\": \"spawn\",\n",
+    "    \"random_seed\": 1040,\n",
+    "    # \"cores\": 1\n",
+    "}\n",
+    "priors = {\n",
+    "    \"rho\": [0.0, 0.5],\n",
+    "    \"alpha\": [0, 3],\n",
+    "    \"beta_O\": [0, 3],\n",
+    "    \"eps\": [0, 1],\n",
+    "    \"sigma_U\": [0.5],\n",
+    "}\n",
+    "priors_no_confounding = {\n",
+    "    \"rho\": [0.0, 0.001],\n",
+    "    \"alpha\": [0, 3],\n",
+    "    \"beta_O\": [0, 3],\n",
+    "    \"eps\": [0, 1],\n",
+    "    \"sigma_U\": [0.5],\n",
+    "}\n",
+    "\n",
+    "nhefs_binary_model = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors,\n",
+    "    observed=True,\n",
+    ")\n",
+    "nhefs_binary_model_cate = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=True,\n",
+    "    cate_estimation=True,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors,\n",
+    "    observed=True,\n",
+    ")\n",
+    "nhefs_binary_model_0_rho = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors_no_confounding,\n",
+    "    observed=True,\n",
+    ")\n",
+    "\n",
+    "nhefs_binary_model_s_s = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors,\n",
+    "    observed=True,\n",
+    "    spike_and_slab=True,\n",
+    ")\n",
+    "\n",
+    "with nhefs_binary_model:\n",
+    "    idata_nhefs = pm.sample_prior_predictive()\n",
+    "    idata_nhefs.extend(\n",
+    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
+    "    )\n",
+    "\n",
+    "with nhefs_binary_model_cate:\n",
+    "    idata_nhefs_cate = pm.sample_prior_predictive()\n",
+    "    idata_nhefs_cate.extend(\n",
+    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
+    "    )\n",
+    "\n",
+    "with nhefs_binary_model_0_rho:\n",
+    "    idata_nhefs_0_rho = pm.sample_prior_predictive()\n",
+    "    idata_nhefs_0_rho.extend(\n",
+    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
+    "    )\n",
+    "\n",
+    "with nhefs_binary_model_s_s:\n",
+    "    idata_nhefs_s_s = pm.sample_prior_predictive()\n",
+    "    idata_nhefs_s_s.extend(\n",
+    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The predictive comparison shows effectively comparable model performance metrics, with the linear model exhibited the \"best\" metric. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_df = az.compare(\n",
+    "    {\n",
+    "        \"nhefs_binary_linear\": idata_nhefs,\n",
+    "        \"nhefs_bart_cate\": idata_nhefs_cate,\n",
+    "        \"nhefs_rho_0\": idata_nhefs_0_rho,\n",
+    "        \"nhefs_s_s\": idata_nhefs_s_s,\n",
+    "    },\n",
+    "    var_name=\"likelihood_outcome\",\n",
+    ")\n",
+    "\n",
+    "az.plot_compare(compare_df);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters are well identified across all model specifications. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    \n",
+       "  \n",
+       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
nhefs_binary_linearalpha6.8190.4216.0227.5770.0120.0061324.02339.01.01
rho-0.1750.025-0.220-0.1270.0010.000931.02193.01.01
nhefs_bart_catealpha8.3800.6217.2189.5470.0370.015283.0769.01.01
rho-0.2290.043-0.311-0.1500.0030.001251.0673.01.01
nhefs_rho_0alpha5.1700.3724.4795.8810.0070.0052808.02768.01.00
rho-0.0000.001-0.0020.0020.0000.0007234.02770.01.00
nhefs_slate_slabalpha6.6830.4265.8327.4400.0140.007994.01915.01.00
rho-0.1700.025-0.216-0.1230.0010.000772.01334.01.00
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "nhefs_binary_linear alpha  6.819  0.421   6.022    7.577      0.012    0.006   \n",
+       "                    rho   -0.175  0.025  -0.220   -0.127      0.001    0.000   \n",
+       "nhefs_bart_cate     alpha  8.380  0.621   7.218    9.547      0.037    0.015   \n",
+       "                    rho   -0.229  0.043  -0.311   -0.150      0.003    0.001   \n",
+       "nhefs_rho_0         alpha  5.170  0.372   4.479    5.881      0.007    0.005   \n",
+       "                    rho   -0.000  0.001  -0.002    0.002      0.000    0.000   \n",
+       "nhefs_slate_slab    alpha  6.683  0.426   5.832    7.440      0.014    0.007   \n",
+       "                    rho   -0.170  0.025  -0.216   -0.123      0.001    0.000   \n",
+       "\n",
+       "                           ess_bulk  ess_tail  r_hat  \n",
+       "nhefs_binary_linear alpha    1324.0    2339.0   1.01  \n",
+       "                    rho       931.0    2193.0   1.01  \n",
+       "nhefs_bart_cate     alpha     283.0     769.0   1.01  \n",
+       "                    rho       251.0     673.0   1.01  \n",
+       "nhefs_rho_0         alpha    2808.0    2768.0   1.00  \n",
+       "                    rho      7234.0    2770.0   1.00  \n",
+       "nhefs_slate_slab    alpha     994.0    1915.0   1.00  \n",
+       "                    rho       772.0    1334.0   1.00  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat(\n",
+    "    {\n",
+    "        \"nhefs_binary_linear\": az.summary(idata_nhefs, var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"nhefs_bart_cate\": az.summary(idata_nhefs_cate, var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"nhefs_rho_0\": az.summary(idata_nhefs_0_rho, var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"nhefs_slate_slab\": az.summary(idata_nhefs_s_s, var_names=[\"alpha\", \"rho\"]),\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "resulting in a treatment effect estimate greater than the baseline OLS assumption. We can also assess the Bayes factor i.e. the comparison of each model under a particular null hypothesis. Here we compare each model to the null of the OLS estimate. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "arviz - WARNING - The reference value is outside of the posterior. This translate into infinite support for H1, which is most likely an overstatement.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2, 2, figsize=(20, 6))\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "feature_str = \" + \".join(features)\n",
+    "formula = \"outcome ~ trt +\" + feature_str\n",
+    "res = smf.ols(\n",
+    "    formula,\n",
+    "    df_nhefs,\n",
+    ").fit()\n",
+    "ols_est = res.params[\"trt\"]\n",
+    "\n",
+    "az.plot_bf(idata_nhefs, var_name=\"alpha\", ref_val=ols_est, ax=axs[0])\n",
+    "az.plot_bf(idata_nhefs_cate, var_name=\"alpha\", ref_val=ols_est, ax=axs[1])\n",
+    "az.plot_bf(idata_nhefs_0_rho, var_name=\"alpha\", ref_val=ols_est, ax=axs[2])\n",
+    "az.plot_bf(idata_nhefs_s_s, var_name=\"alpha\", ref_val=ols_est, ax=axs[3])\n",
+    "axs[0].set_xlabel(\"\"\" alpha \\n Linear Model \"\"\")\n",
+    "axs[1].set_xlabel(\"\"\" alpha \\n BART CATE Model \"\"\")\n",
+    "axs[2].set_xlabel(\"\"\" alpha \\n Rho at 0 Model \"\"\")\n",
+    "axs[3].set_xlabel(\"\"\" alpha \\n Linear Spike and Slab \"\"\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results all indicate a positive effect on weight due to the quitting smoking. They vary slightly in the attributed effect but, interestingly even if we try to zero out the correlation between treatment and outcome the model still implies a higher effect than observed in the simpler regression model. The Bayes factor plots report that the alternative hypothesis $\\alpha \\neq 3.3$ is between 5 and 40 times more likely than the null hypothesis of  $\\alpha = 3.3$. They also indicate the effect of Bayesian updating by the extent in which the posterior has transformed from the prior in each plot. Interestingly, these array of results suggests the treatment estimate is sensitive to the endogeneity correction. Allowing $\\rho \\neq 0$ increases the estimated effect (from ~5 to ~6). That means the joint model is using the estimated correlation between unobservables to further adjust the treatment coefficient beyond what the model with $\\rho = 0$ does. To top this off, the sign of the $\\rho$ estimate matters. When $\\rho$ is allowed to vary, the posterior estimate forces the correlation between treatment propensity and outcome measure negative. In other words, the propensity to quit is associated with _less weight gain_. By modelling this relationship as a correlation, the $\\alpha$ parameter is not forced to reflect this pattern, and the model can attribute a higher effect to the treatment intervention than we found with the OLS estimate. \n",
+    "\n",
+    "### Applying These Methods\n",
+    "\n",
+    "The models demonstrated here are not recipes to be followed mechanically but frameworks for making structural assumptions explicit. Before fitting a Bayesian causal model to real data, ask yourself three questions:\n",
+    "\n",
+    "**First: Can I defend my causal structure theoretically?** Which variables do you believe are confounders, which are instruments, which are irrelevant? Write down your causal graph before writing down your priors. If you cannot justify exclusion restrictions through domain knowledge or institutional understanding, data-driven variable selection will not rescue you—it will merely dress speculation in statistical clothing.\n",
+    "\n",
+    "**Second: How sensitive are my conclusions to structural assumptions?** The confounding parameter $\\rho$ is rarely identified from observables alone. Vary your priors on ρ across plausible ranges and observe how your treatment effect estimate shifts. Fit models with normal priors, sparse priors, and theory-driven exclusions. If your causal conclusions are stable across specifications, they're robust. If they vary dramatically, that variation is real epistemic uncertainty and should be reported as such.\n",
+    "\n",
+    "**Third: Where have I placed flexibility, and why?** Automated variable selection and nonparametric methods are powerful tools, but flexibility in the outcome equation can absorb the causal effects you're trying to estimate. As we demonstrated with BART, sufficiently flexible outcome models learn total associations rather than structural parameters. Use flexibility in the treatment equation if needed, but keep the outcome equation constrained to interpretable causal parameters.\n",
+    "\n",
+    "### Conclusion\n",
+    "\n",
+    "These questions point to what distinguishes structural modeling from purely associational approaches. When we specify a Bayesian causal model, we write down a probabilistic program that encodes our beliefs about how data are generated—which variables influence which, how uncertainty enters, what exclusions hold. Once fitted, the model becomes a working machine we can run forward under interventions, perturb in its assumptions, and interrogate for consequences. This executable character lets us simulate alternative worlds and test the coherence of our causal story, rather than merely report coefficients.\n",
+    "\n",
+    "The virtue of treating causal models as probabilistic programs is twofold. First, it forces us to articulate our causal beliefs explicitly i.e. the graphical, functional, and stochastic components that make the model run. Second, it offers a disciplined way to explore what follows from those beliefs under uncertainty. Bayesian structural causal inference therefore unites an epistemic modesty with computational rigor: each model is a local, provisional machine for generating causal understanding, not a final map of the world.\n",
+    "\n",
+    "The credibility revolution's achievement was recognizing that causal claims require more than correlations. Causal inference requires identification strategies. These strategies try to bracket complexity through design. Bayesian structural modeling takes a complementary path: it models complexity explicitly, then explores how robust our conclusions are to structural perturbations. Both approaches succeed when we know not only how our models work, but where they stop working. \n",
+    "\n",
+    "Every causal model, like every fish tank, is a \"small world\" whose regularities we can nurture but never universalize. Our task is not to master the ocean, but to build clear tanks and learn when to change the water.\n",
+    "\n",
+    "### References\n",
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "CausalPy",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/references.bib b/docs/source/references.bib
index 207d0e21..9460e6c4 100644
--- a/docs/source/references.bib
+++ b/docs/source/references.bib
@@ -23,6 +23,17 @@ @article{richardson2009monetary
   publisher={The University of Chicago Press}
 }
 
+
+@book{pearl2000causality,
+  title={Causality: Models, Reasoning, and Inference},
+  author={Pearl, J.},
+  isbn={9780521773621},
+  lccn={99042108},
+  url={https://books.google.ie/books?id=wnGU_TsW3BQC},
+  year={2000},
+  publisher={Cambridge University Press}
+}
+
 @book{angrist2014mastering,
   title={Mastering 'Metrics: The path from cause to effect},
   author={Angrist, Joshua D and Pischke, J{\"o}rn-Steffen},
@@ -30,6 +41,16 @@ @book{angrist2014mastering
   publisher={Princeton University Press}
 }
 
+@book{angrist2009mostly,
+  title={Mostly Harmless Econometrics: An Empiricist's Companion},
+  author={Angrist, J.D. and Pischke, J.S.},
+  isbn={9780691120355},
+  lccn={2008036265},
+  url={https://books.google.ie/books?id=YSAzEAAAQBAJ},
+  year={2009},
+  publisher={Princeton University Press}
+}
+
 @article{carpenter2009effect,
   title={The effect of alcohol consumption on mortality: regression discontinuity evidence from the minimum drinking age},
   author={Carpenter, Christopher and Dobkin, Carlos},
@@ -59,6 +80,13 @@ @book{hansenEconometrics
   publisher={Princeton}
 }
 
+@book{kaplan_bs_social_science,
+  title={Bayesian Statistics for the social sciences},
+  author={Kaplan, David},
+  year={2024},
+  publisher={Guilford Press}
+}
+
 @book{aronowFoundations,
   author={Aronow, P and  Miller, B},
   title={Foundations of Agnostic Statistics},
diff --git a/environment.yml b/environment.yml
index 09850e05..bf0fcee4 100644
--- a/environment.yml
+++ b/environment.yml
@@ -16,4 +16,5 @@ dependencies:
   - statsmodels
   - xarray>=v2022.11.0
   - pymc-extras>=0.3.0
+  - pymc-bart
   - python>=3.11