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Bandwidth Selection for Treatment Choice with Binary Outcomes

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  • Takuya Ishihara

Abstract

This study considers the treatment choice problem when outcome variables are binary. We focus on statistical treatment rules that plug in fitted values based on nonparametric kernel regression and show that optimizing two parameters enables the calculation of the maximum regret. Using this result, we propose a novel bandwidth selection method based on the minimax regret criterion. Finally, we perform a numerical analysis to compare the optimal bandwidth choices for the binary and normally distributed outcomes.

Suggested Citation

  • Takuya Ishihara, 2023. "Bandwidth Selection for Treatment Choice with Binary Outcomes," Papers 2308.14375, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2308.14375
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    References listed on IDEAS

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    1. Tetenov, Aleksey, 2012. "Statistical treatment choice based on asymmetric minimax regret criteria," Journal of Econometrics, Elsevier, vol. 166(1), pages 157-165.
    2. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
    3. Takuya Ishihara & Toru Kitagawa, 2021. "Evidence Aggregation for Treatment Choice," Papers 2108.06473, arXiv.org, revised Jul 2024.
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