发表机构
Grinnell College; Indiana University Bloomington（格林内尔学院; 印第安纳大学布卢明顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个结合边际处理效应与相干风险度量的框架，用于风险规避的福利最大化处理分配，并证明其最优规则及有限样本保证，应用于大学录取政策显示风险规避带来显著变化。
AI 中文摘要
本文研究当个体根据未观测特征自我选择接受处理时的风险规避处理分配问题。我们开发了一个框架，将内生选择的边际处理效应方法与一类一般性的相干风险度量相结合，这些风险度量捕捉对福利结果的分布性偏好。我们证明，规划者的问题在不确定性厌恶、分布稳健性和最坏情况福利方面具有等价解释。对于律不变的相干风险度量，我们推导出一个Kusuoka表示，将规划者的目标表达为对福利分布不同区域的加权评估，并刻画由此产生的最优分配规则。我们进一步为经验风险规避策略学习建立了有限样本遗憾保证，展示了学习策略的统计难度如何取决于规划者对不利福利结果的敏感性。该框架将\cite{Kiatagawa_Tetenov_2018}的风险中性策略学习模型作为特例包含在内。对\cite{Card1995}大学邻近数据的应用表明，纳入风险规避可能导致最优大学录取政策产生经济上有意义的改变。
英文摘要
This paper studies risk-averse treatment allocation when individuals self-select into treatment based on unobserved characteristics. We develop a framework that combines the marginal treatment effect approach to endogenous selection with a general class of coherent risk measures that capture distributional preferences over welfare outcomes. We show that the planner's problem admits equivalent interpretations in terms of uncertainty aversion, distributional robustness, and worst-case welfare. For law-invariant coherent risk measures, we derive a Kusuoka representation that expresses the planner's objective as a weighted evaluation of different regions of the welfare distribution and characterize the resulting optimal allocation rule. We further establish finite-sample regret guarantees for empirical risk-averse policy learning, showing how the statistical difficulty of learning a policy depends on the planner's sensitivity to adverse welfare outcomes. The framework nests the risk-neutral policy learning model of \cite{Kiatagawa_Tetenov_2018} as a special case. An application to the \cite{Card1995} college proximity data demonstrates that incorporating risk aversion can lead to economically meaningful changes in optimal college admission policies.
