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Policy learning with new treatments

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  • Samuel D. Higbee

Abstract

I study the problem of a decision maker choosing a policy that 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 that the rate at which the maximum regret of the estimated policy converges to the lowest possible maximum regret is the maximum of N−1/2 and the rate at which conditional average treatment effects are estimated in the experimental data. In an application to designing targeted subsidies for electrical grid connections in rural Kenya, I find that nearly the entire population should be given a treatment not implemented in the experiment, reducing maximum regret by over 60% compared to the policy that restricts to the treatments implemented in the experiment.

Suggested Citation

  • Samuel D. Higbee, 2025. "Policy learning with new treatments," Quantitative Economics, Econometric Society, vol. 16(4), pages 1409-1456, November.
    • Handle: RePEc:wly:quante:v:16:y:2025:i:4:p:1409-1456
    DOI: 10.3982/QE2477
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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