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Efficient Estimation for Staggered Rollout Designs

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  • Jonathan Roth
  • Pedro H. C. Sant'Anna

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.

Suggested Citation

  • Jonathan Roth & Pedro H. C. Sant'Anna, 2021. "Efficient Estimation for Staggered Rollout Designs," Papers 2102.01291, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2102.01291
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