Conditional Triple Difference-in-Differences

Dor Leventer

Papers from arXiv.org

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.

Date: 2025-02, Revised 2025-02
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