March 15, 2019

Double-Robust Estimation in Difference-in-Differences with an Application to Traffic Safety Evaluation

By Fan Li and Fan Li


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.

Applications and Case Studies Methodology