Update README.md

Author: junyuan-chen

README.md

@@ -11,68 +11,83 @@ across multiple treatment groups over multiple time periods.
 It is a component of [DiffinDiffs.jl](https://github.com/JuliaDiffinDiffs/DiffinDiffs.jl)
 that can also be used as a standalone package.
 
+> **Note:**
+>
+> The development of this package is still in an early stage.
+> Not all features are production-ready.
+
 ## Applicable Environment
 
 The baseline DID setup this package focuses on
-is the same as the one considered by [Sun and Abraham (2020)](#SunA20):
+is the same as the one considered by [Sun and Abraham (2020)](https://doi.org/10.1016/j.jeconom.2020.09.006):
 
 * The treatment state is binary, irreversible and sharp.
 * Units are treated in *different* periods (possibly never treated) in a staggered fashion.
-* Treatment effects may evolve over time following possibly different paths across groups.
+* Treatment effects may evolve over time following possibly different paths across treated groups.
 
 The parameters of interest include:
 
 * A collection of average treatment effects
 on each group of treated units in different periods.
 * Interpretable aggregations of these group-time-level parameters.
 
-## Purposes and Functionality
+## Motivation and Features
 
-Although it is possible to accomplish the estimation goals
-by directly working with the regression functionality in any statistical software,
-the amount of work involved can be nontrivial.
+Although most estimation tasks covered by this package
+can be conducted with existing statistical software,
+the amount of programming work involved can be nontrivial.
 Ad hoc implementation for a specific study
 may be prone to programming errors,
 not reusable for future projects and also computationally inefficient.
-A package that fills in the gap
-between data preparation and estimation procedures is therefore desirable.
+Some extra amount of automation in conducting the estimation procedures
+is therefore desirable.
+This is especially important when the estimation involves many regressors
+and the dataset is relatively large.
 
-Some main functionality provided by this package includes:
+This package aims to raise the productivity of empirical researchers
+with the following features:
 
-* Memory-efficient generation of indicator variables needed for estimation based on data coverage.
+* Automatic and efficient generation of indicator variables based on empirical design and data coverage.
 * Enforcement of an overlap condition based on the parallel trends assumption.
 * Fast residualization of regressors from fixed effects via [FixedEffects.jl](https://github.com/FixedEffects/FixedEffects.jl).
-* Interaction-weighted DID estimators as proposed by [Sun and Abraham (2020)](#SunA20).
-* Cell-level weight calculations for decomposing estimates from regression.
+* Interaction-weighted DID estimators proposed by [Sun and Abraham (2020)](https://doi.org/10.1016/j.jeconom.2020.09.006).
+* Cell-level weight calculations for decomposing coefficient estimates from regression.
 
 As a component of [DiffinDiffs.jl](https://github.com/JuliaDiffinDiffs/DiffinDiffs.jl),
 it follows the same programming interface shared by all component packages.
-In particular, it is benefited from the `@specset` macro
-that largely simplifies the construction of groups of related specifications
-and avoids unnecessary repetitions of the same intermediate steps
-(e.g., partialling out fixed effects).
-Tools for easing the export of estimation results are also available.
+In particular, it is benefited from the macros `@did` and `@specset`
+that largely simplify the construction of groups of related specifications
+and reduce unnecessary repetitions of identical intermediate steps
+(e.g., partialling out fixed effects for the same regressors).
+Tools for easing the export of estimation results are also being developed.
 
 ## Econometric Foundations
 
 The package does not enforce the use of a specific estimation procedure
-and allows flexible usage from the users.
-However, it is designed to ease the adoption of
+and allows flexibility from the users.
+However, it is mainly designed to ease the adoption of
 recent advances in econometric research
 that overcome pitfalls in earlier empirical work.
 
-The most relevant econometric studies that provide theoretical guidance
-are the following:
+The development of this package is directly based on the following studies:
+* [Sun and Abraham (2020)](https://doi.org/10.1016/j.jeconom.2020.09.006)
+* Unpublished work by the package author
+
+Some other studies are also relevant and have provided inspiration:
 
-* [Sun and Abraham (2020)](#SunA20)
-* [Goodman-Bacon (2020)](#Goodman20)
+* [de Chaisemartin and D'Haultfœuille (2020)](https://doi.org/10.1257/aer.20181169)
 * [Borusyak and Jaravel (2018)](#BorusyakJ18)
-* Unpublished work by the package author
+* [Goodman-Bacon (2020)](#Goodman20)
+* [Callaway and Sant'Anna (2020)](https://doi.org/10.1016/j.jeconom.2020.12.001)
 
 ## References
 
 <a name="BorusyakJ18">**Borusyak, Kirill, and Xavier Jaravel.** 2018. "Revisiting Event Study Designs with an Application to the Estimation of the Marginal Propensity to Consume." Unpublished.</a>
 
+<a name="CallawayS20">**Callaway, Brantly, and Pedro H. C. Sant'Anna.** 2020. "Difference-in-Differences with Multiple Time Periods." *Journal of Econometrics*, forthcoming.</a>
+
+<a name="ChaisemartD20T">**de Chaisemartin, Clément, and Xavier D'Haultfœuille.** 2020. "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects." *American Economic Review* 110 (9): 2964-96.</a>
+
 <a name="Goodman20">**Goodman-Bacon, Andrew.** 2020. "Difference-in-Differences with Variation in Treatment Timing." Unpublished.</a>
 
 <a name="SunA20">**Sun, Liyang, and Sarah Abraham.** 2020. "Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects." *Journal of Econometrics*, forthcoming.</a>