Estimation of Direct and Indirect Quantile Treatment Effects with Double Machine Learning
提出框架将二元处理在特定秩上的分位数处理效应分解为通过中介变量传导的间接效应和直接效应，并基于双重机器学习方法进行估计，应用于职业培训对收入影响的分析。
We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of potential outcomes. We demonstrate uniform consistency and asymptotic normality of our effect estimators under specific regularity conditions and propose a multiplier bootstrap for statistical inference. Finally, we apply our method to data from the National Job Corps Study to assess the direct effect of training on earnings and the indirect effect operating through work experience.