Efficient Estimation for Staggered Rollout Designs

Jonathan Roth and Pedro Sant'Anna ()

Papers from arXiv.org

Abstract: We study estimation of causal effects in staggered rollout designs, i.e. settings where there is staggered treatment adoption and the timing of treatment is as-good-as randomly assigned. We derive the most efficient estimator in a class of estimators that nests several popular generalized difference-in-differences methods. A feasible plug-in version of the efficient estimator is asymptotically unbiased with efficiency (weakly) dominating that of existing approaches. We provide both $t$-based and permutation-test-based methods for inference. In an application to a training program for police officers, confidence intervals for the proposed estimator are as much as eight times shorter than for existing approaches.

Date: 2021-02, Revised 2023-05
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Citations: View citations in EconPapers (13)

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Journal Article: Efficient Estimation for Staggered Rollout Designs (2023)
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