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Bandwidth selection for treatment choice with binary outcomes

Author

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  • Takuya Ishihara

    (Tohoku University)

Abstract

This study considers the treatment choice problem when the outcome variable is binary. We focus on statistical treatment rules that plug in fitted values from a nonparametric kernel regression, and show that the maximum regret can be calculated by maximizing over two parameters. Using this result, we propose a novel bandwidth selection method based on the minimax regret criterion. Finally, we perform a numerical exercise to compare the optimal bandwidth choices for binary and normally distributed outcomes.

Suggested Citation

  • Takuya Ishihara, 2023. "Bandwidth selection for treatment choice with binary outcomes," The Japanese Economic Review, Springer, vol. 74(4), pages 539-549, October.
    • Handle: RePEc:spr:jecrev:v:74:y:2023:i:4:d:10.1007_s42973-023-00142-5
    DOI: 10.1007/s42973-023-00142-5
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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. Tetenov, Aleksey, 2012. "Statistical treatment choice based on asymmetric minimax regret criteria," Journal of Econometrics, Elsevier, vol. 166(1), pages 157-165.
    3. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
    4. Charles F Manski, 2007. "Adaptive Minimax-Regret Treatment Choice, with Application to Drug Approval," Levine's Working Paper Archive 122247000000001404, David K. Levine.
    5. Manski, Charles F., 2007. "Minimax-regret treatment choice with missing outcome data," Journal of Econometrics, Elsevier, vol. 139(1), pages 105-115, July.
    6. 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.
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