A difference-in-differences estimator by covariate balancing propensity score

TL;DR

This paper introduces a covariate balancing propensity score method for difference-in-differences analysis, offering improved efficiency, robustness, and convergence properties for estimating treatment effects on the treated in panel data.

Contribution

It develops a novel CBPS DID estimator with theoretical advantages over existing methods, including double robustness and faster convergence.

Findings

Demonstrates superior finite sample performance in simulations

Shows theoretical double robustness and local efficiency

Outperforms AIPW DID estimators in convergence speed

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.