标题： （机器）学习什么策略有价值 显示英文标题

标题： (Machine) Learning What Policies Value

摘要： 当一项政策优先考虑一个人而非另一个人时，是因为他们受益更多，还是因为他们更受偏爱？ 本文提出了一种方法，以揭示与观察到的分配决策一致的价值观。 我们使用机器学习方法来估计每个人从干预中获得的利益，然后将其分配与（i）不同人的福利权重；（ii）干预的异质处理效果；以及（iii）不同结果的权重相协调。 我们通过分析墨西哥的PROGRESA反贫困计划来演示这种方法。 分析表明，尽管该计划优先考虑某些子群体——如土著家庭——但这些群体受益更多的事实意味着他们实际上被赋予了较低的福利权重。 PROGRESA案例说明了该方法如何使审计现有政策成为可能，并设计未来更能与价值观一致的政策。 显示英文摘要

摘要： When a policy prioritizes one person over another, is it because they benefit more, or because they are preferred? This paper develops a method to uncover the values consistent with observed allocation decisions. We use machine learning methods to estimate how much each individual benefits from an intervention, and then reconcile its allocation with (i) the welfare weights assigned to different people; (ii) heterogeneous treatment effects of the intervention; and (iii) weights on different outcomes. We demonstrate this approach by analyzing Mexico's PROGRESA anti-poverty program. The analysis reveals that while the program prioritized certain subgroups -- such as indigenous households -- the fact that those groups benefited more implies that they were in fact assigned a lower welfare weight. The PROGRESA case illustrates how the method makes it possible to audit existing policies, and to design future policies that better align with values.