Treatment Choice with Nonlinear Regret

Toru Kitagawa, Sokbae (Simon) Lee and Chen Qiu

Papers from arXiv.org

Abstract: The literature focuses on the mean of welfare regret, which can lead to undesirable treatment choice due to sensitivity to sampling uncertainty. We propose to minimize the mean of a nonlinear transformation of regret and show that singleton rules are not essentially complete for nonlinear regret. Focusing on mean square regret, we derive closed-form fractions for finite-sample Bayes and minimax optimal rules. Our approach is grounded in decision theory and extends to limit experiments. The treatment fractions can be viewed as the strength of evidence favoring treatment. We apply our framework to a normal regression model and sample size calculation.

Date: 2022-05, Revised 2024-10
New Economics Papers: this item is included in nep-dcm
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