Doubly Robust Difference-in-Differences Estimators
Pedro Sant'Anna () and
Jun B. Zhao
Papers from arXiv.org
This article proposes a doubly robust estimation procedure for the average treatment effect on the treated in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, our proposed estimators are consistent if either (but not necessarily both) a propensity score model or outcome regression models are correctly specified. In addition, our proposed methodology accommodates linear and nonlinear specifications, allows for treatment effect heterogeneity, and can be applied with either panel or repeated cross section data. We establish the asymptotic distribution of our proposed doubly robust estimators, and propose a computationally simple bootstrap procedure to conduct asymptotically valid inference. Our inference procedures directly account for multiple testing, and are therefore suitable in situations where researchers are interested in the effect of a given policy on many different outcomes. We demonstrate the relevance of our proposed policy evaluation tools in two different applications.
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