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Policy Learning with New Treatments

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  • Samuel Higbee

Abstract

I study the problem of a decision maker choosing a policy which allocates treatment to a heterogeneous population on the basis of experimental data that includes only a subset of possible treatment values. The effects of new treatments are partially identified by shape restrictions on treatment response. Policies are compared according to the minimax regret criterion, and I show that the empirical analog of the population decision problem has a tractable linear- and integer-programming formulation. I prove the maximum regret of the estimated policy converges to the lowest possible maximum regret at a rate which is the maximum of N^-1/2 and the rate at which conditional average treatment effects are estimated in the experimental data. I apply my results to design targeted subsidies for electrical grid connections in rural Kenya, and estimate that 97% of the population should be given a treatment not implemented in the experiment.

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  • Samuel Higbee, 2022. "Policy Learning with New Treatments," Papers 2210.04703, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2210.04703
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    References listed on IDEAS

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    1. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    2. Hongming Pu & Bo Zhang, 2021. "Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 318-345, April.
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    Cited by:

    1. Lihua Lei & Roshni Sahoo & Stefan Wager, 2023. "Policy Learning under Biased Sample Selection," Papers 2304.11735, arXiv.org.

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