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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

    as
    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. Manski, Charles F., 2007. "Minimax-regret treatment choice with missing outcome data," Journal of Econometrics, Elsevier, vol. 139(1), pages 105-115, July.
    4. Stoye, Jörg, 2009. "Minimax regret treatment choice with finite samples," Journal of Econometrics, Elsevier, vol. 151(1), pages 70-81, July.
    5. Charles F Manski, 2007. "Adaptive Minimax-Regret Treatment Choice, with Application to Drug Approval," Levine's Working Paper Archive 122247000000001404, David K. Levine.
    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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