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Covariate-Balanced Weighted Stacked Difference-in-Differences

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  • Vadim Ustyuzhanin

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.

Suggested Citation

  • Vadim Ustyuzhanin, 2026. "Covariate-Balanced Weighted Stacked Difference-in-Differences," Papers 2604.02293, arXiv.org.
    • Handle: RePEc:arx:papers:2604.02293
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    1. Trounstine, Jessica, 2020. "The Geography of Inequality: How Land Use Regulation Produces Segregation," American Political Science Review, Cambridge University Press, vol. 114(2), pages 443-455, May.
    2. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(1), pages 25-46, January.
    3. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    4. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
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