Pedro H. C. Sant'Anna, Jun B. Zhao
arXiv 29 Nov 2018 · Econometrics · publishedJournal of Econometrics (2020) · 976 citations (OpenAlex)
arXiv:1812.01723 · PDF · DOI · OpenAlex · Extracted main text
This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but not necessarily both) a propensity score or outcome regression working models are correctly specified. We also derive the semiparametric efficiency bound for the ATT in DID designs when either panel or repeated cross-section data are available, and show that our proposed estimators attain the semiparametric efficiency bound when the working models are correctly specified. Furthermore, we quantify the potential efficiency gains of having access to panel data instead of repeated cross-section data. Finally, by paying articular attention to the estimation method used to estimate the nuisance parameters, we show that one can sometimes construct doubly robust DID estimators for the ATT that are also doubly robust for inference. Simulation studies and an empirical application illustrate the desirable finite-sample performance of the proposed estimators. Open-source software for implementing the proposed policy evaluation tools is available.
appendix boundary found by appendix_titled_section at “Appendix A: Asymptotic Properties of the DR DID estimators based on generic first-step estimators\label{App:conditions}” · 83% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Heckman, Ichimura \ Todd (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme | 1.000 | 6 | 4 | 100% |
| 2 | Horvitz \ Thompson (1952) A Generalization of Sampling Without Replacement From a Finite Universe | 1.000 | 5 | 3 | 100% |
| 3 | Abadie (2005) Semiparametric difference-in-difference estimators | 0.928 | 10 | 5 | 80% |
| 4 | Zimmert (2019) Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding | 0.928 | 4 | 3 | 100% |
| 5 | Graham, Pinto \ Egel (2012) Inverse Probability Tilting for Moment Condition Models with Missing Data | 0.874 | 6 | 3 | 67% |
| 6 | Chen, Hong \ Tarozzi (2008) Semiparametric efficiency in GMM models with auxiliary data | 0.874 | 5 | 2 | 100% |
| Hajek1971 | unmatched citation key Hajek1971 | 0.811 | 4 | 2 | 100% |
| 8 | Vermeulen \ Vansteelandt (2015) Bias-Reduced Doubly Robust Estimation | 0.811 | 4 | 2 | 100% |
| Belloni2017 | unmatched citation key Belloni2017 | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey \ Robins (2017) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
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