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Conditional Triple Difference-in-Differences

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  • Dor Leventer

Abstract

Triple difference-in-differences (TDID) designs are widely used in empirical research to estimate causal effects. In practice, most implementations rely on a specification with controls. However, we show that such approaches introduce bias due to differences in covariate distributions across groups. To address this issue, we propose a re-weighted estimator that correctly identifies a causal estimand of interest by aligning covariate distributions across groups. For estimation we develop a double-robust approach. A R package is provided for general use.

Suggested Citation

  • Dor Leventer, 2025. "Conditional Triple Difference-in-Differences," Papers 2502.16126, arXiv.org, revised Feb 2025.
    • Handle: RePEc:arx:papers:2502.16126
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    References listed on IDEAS

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