  Difference-in-differences (DID) is a widely used approach for drawing causal
inference from observational panel data. Two common estimation strategies for
DID are outcome regression and propensity score weighting. In this paper,
motivated by a real application in traffic safety research, we propose a new
double-robust DID estimator that hybridizes regression and propensity score
weighting. We particularly focus on the case of discrete outcomes. We show that
the proposed double-robust estimator possesses the desirable large-sample
robustness property. We conduct a simulation study to examine its finite-sample
performance and compare with alternative methods. Our empirical results from a
Pennsylvania Department of Transportation data suggest that rumble strips are
marginally effective in reducing vehicle crashes.
