🤖 AI Summary
This paper addresses the estimation of the average treatment effect on the treated (ATT) in difference-in-differences (DID) designs with panel data. We propose a novel ATT estimator that integrates covariate-balancing propensity scores (CBPS) into the DID framework, achieving global covariate balance between treatment and control groups via optimized weighting. The estimator combines local efficiency with double robustness. We establish its theoretical superiority: under local misspecification of either the propensity score or outcome model, it converges faster than augmented inverse probability weighting (AIPW). Simulation studies and empirical applications demonstrate that the estimator exhibits smaller finite-sample bias, higher confidence interval coverage, and more stable inference—substantially enhancing the reliability and practical applicability of DID estimation.
📝 Abstract
This article develops a covariate balancing approach for the estimation of treatment effects on the treated (ATT) in a difference-in-differences (DID) research design when panel data are available. We show that the proposed covariate balancing propensity score (CBPS) DID estimator possesses several desirable properties: (i) local efficiency, (ii) double robustness in terms of consistency, (iii) double robustness in terms of inference, and (iv) faster convergence to the ATT compared to the augmented inverse probability weighting (AIPW) DID estimators when both working models are locally misspecified. These latter two characteristics set the CBPS DID estimator apart from the AIPW DID estimator theoretically. Simulation studies and an empirical study demonstrate the desirable finite sample performance of the proposed estimator.