Doubly robust difference-in-differences estimators

Sant’Anna, Pedro H.C. and Jun Zhao
Authors registered in the RePEc Author Service: Pedro H. C. Sant'Anna ()

Journal of Econometrics, 2020, vol. 219, issue 1, 101-122

Abstract: 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 particular 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.

Keywords: Causal inference; Difference-in-differences; Natural experiments; Panel data; Repeated cross-section data; Semiparametric efficiency (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (221)

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Working Paper: Doubly Robust Difference-in-Differences Estimators (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:219:y:2020:i:1:p:101-122

DOI: 10.1016/j.jeconom.2020.06.003

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