  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.
