Covariate-Balanced Weighted Stacked Difference-in-Differences

TL;DR

This paper introduces CBWSDID, a new estimator for difference-in-differences that improves treatment-control comparability and handles complex treatment episodes, supported by simulations and real data applications.

Contribution

It extends weighted stacked DID to conditionally parallel trends settings and provides a new framework with an accompanying R package for implementation.

Findings

CBWSDID improves treatment effect estimation in complex settings.

Simulation studies demonstrate the estimator's robustness.

Applications show practical utility in policy analysis.

Abstract

This paper proposes Covariate-Balanced Weighted Stacked Difference-in-Differences (CBWSDID), a design-based extension of weighted stacked DID for settings in which untreated trends may be conditionally rather than unconditionally parallel. The estimator separates within-subexperiment design adjustment from across-subexperiment aggregation: matching or weighting improves treated-control comparability within each stacked subexperiment, while the corrective stacked weights of Wing et al. recover the target aggregate ATT. I show that the same logic extends from absorbing treatment to repeated $0 \to 1$ and $1 \to 0$ episodes under a finite-memory assumption. The paper develops the identifying framework, discusses inference, presents simulation evidence, and illustrates the estimator in applications based on Trounstine (2020) and Acemoglu et al. (2019). Across these examples, CBWSDID serves as a bridge between weighted stacked DID and design-based panel matching. The accompanying R package cbwsdid is available on GitHub.