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On the numerical approximation of minimax regret rules via fictitious play

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  • Patrik Guggenberger
  • Jiaqi Huang

Abstract

Finding numerical approximations to minimax regret treatment rules is of key interest. To do so when potential outcomes are in {0,1} we discretize the action space of nature and apply a variant of Robinson's (1951) algorithm for iterative solutions for finite two-person zero sum games. Our approach avoids the need to evaluate regret of each treatment rule in each iteration. When potential outcomes are in [0,1] we apply the so-called coarsening approach. We consider a policymaker choosing between two treatments after observing data with unequal sample sizes per treatment and the case of testing several innovations against the status quo.

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  • Patrik Guggenberger & Jiaqi Huang, 2025. "On the numerical approximation of minimax regret rules via fictitious play," Papers 2503.10932, arXiv.org.
    • Handle: RePEc:arx:papers:2503.10932
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    References listed on IDEAS

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    1. 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.
    2. Tetenov, Aleksey, 2012. "Statistical treatment choice based on asymmetric minimax regret criteria," Journal of Econometrics, Elsevier, vol. 166(1), pages 157-165.
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