论文标题
通过新治疗的政策学习
Policy Learning with New Treatments
论文作者
论文摘要
我研究了决策者选择一项政策的问题，该政策是基于仅包括可能的治疗值的一部分的实验数据，将治疗分配给异质人群。新疗法的影响部分通过形状限制对治疗反应的部分确定。根据Minimax遗憾标准比较策略，我表明人口决策问题的经验类似物具有可拖动的线性和整数编程公式。我证明，估计的策略的最大遗憾收敛到最低最大的遗憾，即最大值n^-1/2的速率以及实验数据中估计条件平均治疗效果的速率。在针对肯尼亚农村电网连接的有针对性补贴的应用程序中，我发现几乎应该给出了整个人群在实验中未实施的治疗，与限制了实验中实施的治疗的政策相比，最大的遗憾将超过60％。
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. 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.
