Corpus ID: 88524358

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

@article{Li2019DoubleRobustEI,
  title={Double-Robust Estimation in Difference-in-Differences with an Application to Traffic Safety Evaluation.},
  author={F. Li},
  journal={arXiv: Applications},
  year={2019}
}
  • F. Li
  • Published 2019
  • Mathematics, Computer Science
  • arXiv: Applications
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… Expand
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