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Minimax-Regret Sample Selection in Randomized Experiments

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  • Yuchen Hu
  • Henry Zhu
  • Emma Brunskill
  • Stefan Wager

Abstract

Randomized controlled trials are often run in settings with many subpopulations that may have differential benefits from the treatment being evaluated. We consider the problem of sample selection, i.e., whom to enroll in a randomized trial, such as to optimize welfare in a heterogeneous population. We formalize this problem within the minimax-regret framework, and derive optimal sample-selection schemes under a variety of conditions. Using data from a COVID-19 vaccine trial, we also highlight how different objectives and decision rules can lead to meaningfully different guidance regarding optimal sample allocation.

Suggested Citation

  • Yuchen Hu & Henry Zhu & Emma Brunskill & Stefan Wager, 2024. "Minimax-Regret Sample Selection in Randomized Experiments," Papers 2403.01386, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2403.01386
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    References listed on IDEAS

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    1. Stoye, Jörg, 2009. "Minimax regret treatment choice with finite samples," Journal of Econometrics, Elsevier, vol. 151(1), pages 70-81, July.
    2. Keisuke Hirano & Jack R. Porter, 2009. "Asymptotics for Statistical Treatment Rules," Econometrica, Econometric Society, vol. 77(5), pages 1683-1701, September.
    3. Herve Moulin, 2004. "Fair Division and Collective Welfare," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262633116, April.
    4. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    5. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
    6. Kaneko, Mamoru & Nakamura, Kenjiro, 1979. "The Nash Social Welfare Function," Econometrica, Econometric Society, vol. 47(2), pages 423-435, March.
    7. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    8. Azevedo, Eduardo M. & Mao, David & Montiel Olea, José Luis & Velez, Amilcar, 2023. "The A/B testing problem with Gaussian priors," Journal of Economic Theory, Elsevier, vol. 210(C).
    9. Nathan Kallus, 2021. "On the optimality of randomization in experimental design: How to randomize for minimax variance and design‐based inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 404-409, April.
    10. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    11. Marcella Alsan & Maya Durvasula & Harsh Gupta & Joshua Schwartzstein & Heidi Williams, 2024. "Representation and Extrapolation: Evidence from Clinical Trials," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(1), pages 575-635.
    12. Eduardo M. Azevedo & Alex Deng & José Luis Montiel Olea & Justin Rao & E. Glen Weyl, 2020. "A/B Testing with Fat Tails," Journal of Political Economy, University of Chicago Press, vol. 128(12), pages 4614-4000.
    13. Stoye, Jörg, 2012. "Minimax regret treatment choice with covariates or with limited validity of experiments," Journal of Econometrics, Elsevier, vol. 166(1), pages 138-156.
    14. Charles F. Manski & Aleksey Tetenov, 2019. "Trial Size for Near-Optimal Choice Between Surveillance and Aggressive Treatment: Reconsidering MSLT-II," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 305-311, March.
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