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Treatment Choice with Nonlinear Regret

Toru Kitagawa, Sokbae Lee, Chen Qiu

arXiv 17 May 2022 · Econometrics

arXiv:2205.08586 · PDF · Extracted main text

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.

Citation extraction

62
references
139
in-text mentions
62
distinct cited
3
self-citations
13,506
main-text words

appendix boundary found by appendix_command · 52% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Hayashi, T (2008) Regret aversion and opportunity dependence1.000134100%
2Manski, C. F (2004) Statistical treatment rules for heterogeneous populations1.00093100%
3Manski, C. F (2021) a): Econometrics for decision making: Building foundations sketched by Haavelmo and Wald0.92843100%
4Tetenov, A (2012) Statistical treatment choice based on asymmetric minimax regret criteria0.92843100%
5Hirano, K. and J. R. Porter (2009) Asymptotics for statistical treatment rules0.84310460%
6Savage, L (1951) The theory of statistical decision0.84333100%
7Wald, A (1950) Statistical Decision Functions0.81142100%
8Stoye, J (2009) Minimax regret treatment choice with finite samples0.81142100%
9Hirano, K. and J. R. Porter (2020) Asymptotic analysis of statistical decision rules in econometrics, in0.73732100%
10Lehmann, E. L. and G. Casella (1998) Theory of point estimation0.6443267%

Showing the top 10 of 62 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Treatment Choice, Mean Square Regret and Partial Identification0.900377
2Regret Analysis in Threshold Policy Design0.64422
3Leave No One Undermined: Policy Targeting with Regret Aversion0.64422
4Selecting the Best Arm in One-Shot Multi-Arm RCTs: The Asymptotic Minimax-Regret Decision Framework for the Best-Population Selection Problem0.64422
5Stochastic treatment choice with empirical welfare updating0.58531
6Inference on Optimal Policy Values and Other Irregular Functionals via Softmax Smoothing0.51121
7Empirical Welfare Maximization with Constraints0.40511
8Orthogonal Policy Learning Under Ambiguity0.40511
9Statistical Treatment Rules under Social Interaction0.40511
10Decision Theory for Treatment Choice Problems with Partial Identification0.40511