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Clustering and External Validity in Randomized Controlled Trials

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  • Antoine Deeb
  • Cl'ement de Chaisemartin

Abstract

The randomization inference literature studying randomized controlled trials (RCTs) assumes that units' potential outcomes are deterministic. This assumption is unlikely to hold, as stochastic shocks may take place during the experiment. In this paper, we consider the case of an RCT with individual-level treatment assignment, and we allow for individual-level and cluster-level (e.g. village-level) shocks. We show that one can draw inference on the ATE conditional on the realizations of the cluster-level shocks, using heteroskedasticity-robust standard errors, or on the ATE netted out of those shocks, using cluster-robust standard errors.

Suggested Citation

  • Antoine Deeb & Cl'ement de Chaisemartin, 2019. "Clustering and External Validity in Randomized Controlled Trials," Papers 1912.01052, arXiv.org, revised Dec 2022.
    • Handle: RePEc:arx:papers:1912.01052
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    References listed on IDEAS

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    7. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
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