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

18 Mar 2019  ·  Li Fan, Li Fan ·

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. read more

PDF Abstract
No code implementations yet. Submit your code now

Categories


Applications

Datasets


  Add Datasets introduced or used in this paper