Matched difference-in-differences estimators: a comparative simulation study

초록

This paper studies the performance of difference-in-differences (DID) estimators when outcome data for untreated units are entirely unobserved and pseudo-controls are constructed via matching with external donors. This study aims to compare the finite-sample performance of several matching-based DID estimators. In doing so, we additionally formulate inverse probability weighting (IPW) and doubly robust (DR) versions within the matching framework, extending existing approaches beyond the commonly used two-way fixed effects (TWFE) and regression adjustment (REG) estimators. Monte Carlo simulations compare the four estimators under varying covariate specifications, matching quality, and violations of the parallel trends assumption. Results show that well-specified matching improves estimation accuracy and robustness. The reliability of DID estimates in treated-only contexts depends critically on the quality of matching.

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