标题： 具有异质处理的异质性分析 显示英文标题

标题： Heterogeneity Analysis with Heterogeneous Treatments

摘要： 在实证处理评估研究中，分析群体层面的效果异质性是标准做法。 然而，所分析的处理通常是多个潜在处理的集合，这些处理本身也是异质的，例如培训项目的不同模块或不同的暴露程度。 在这些情况下，当不同群体之间的潜在处理倾向系统性不同时，传统的方法如比较（调整后的）群体间均值差异可能会产生误导性的结论。 本文提出了一种新的分解框架，能够区分效果异质性和处理异质性的定性不同组成部分对观察到的群体层面差异的贡献。 我们提出了半参数去偏机器学习估计量，这些估计量对复杂处理和有限重叠具有鲁棒性。 我们重新审视了一个广泛记录的积极劳动力市场政策中培训回报的性别差距。 分解结果表明，这几乎完全是由女性受到的处理与男性不同所致，而不是由相同处理的异质回报所致。 具体而言，女性被不成比例地引导至回报较低的职业培训路径。 显示英文摘要

摘要： Analysis of effect heterogeneity at the group level is standard practice in empirical treatment evaluation research. However, treatments analyzed are often aggregates of multiple underlying treatments which are themselves heterogeneous, e.g. different modules of a training program or varying exposures. In these settings, conventional approaches such as comparing (adjusted) differences-in-means across groups can produce misleading conclusions when underlying treatment propensities differ systematically between groups. This paper develops a novel decomposition framework that disentangles contributions of effect heterogeneity and qualitatively distinct components of treatment heterogeneity to observed group-level differences. We propose semiparametric debiased machine learning estimators that are robust to complex treatments and limited overlap. We revisit a widely documented gender gap in training returns of an active labor market policy. The decomposition reveals that it is almost entirely driven by women being treated differently than men and not by heterogeneous returns from identical treatments. In particular, women are disproportionately targeted towards vocational training tracks with lower unconditional returns.