We suggest double/debiased machine learning estimators of direct and indirect quantile treatment effects under a selection-on-observables assumption. This permits disentangling the causal effect of a binary treatment at a specific outcome rank into an indirect component that operates through an intermediate variable called mediator and an (unmediated) direct impact. The proposed method is based on the efficient score functions of the cumulative distribution functions of potential outcomes, which are robust to certain misspecifications of the nuisance parameters, i.e., the outcome, treatment, and mediator models. We estimate these nuisance parameters by machine learning and use cross-fitting to reduce overfitting bias in the estimation of direct and indirect quantile treatment effects. We establish uniform consistency and asymptotic normality of our effect estimators. We also propose a multiplier bootstrap for statistical inference and show the validity of the multiplier bootstrap. Finally, we investigate the finite sample performance of our method in a simulation study and apply it to empirical data from the National Job Corp Study to assess the direct and indirect earnings effects of training.

翻译：我们建议在可观测变量选择假设下，采用双重/去偏机器学习估计量对直接和间接分位数处理效应进行估计。该方法允许将二元处理变量在特定结果分位数上的因果效应分解为两部分：通过中介变量（中间变量）传导的间接成分，以及（非中介的）直接效应。所提方法基于潜在结果累积分布函数的有效得分函数，该函数对干扰参数（即结果模型、处理模型与中介模型）的特定误设定具有稳健性。我们通过机器学习估计这些干扰参数，并采用交叉拟合方法降低直接与间接分位数处理效应估计中的过拟合偏差。建立了效应估计量的一致性与渐近正态性，并提出了用于统计推断的乘子自助法，验证了该方法的有效性。最后通过仿真研究考察了方法的有限样本表现，并将其应用于美国国家职业培训项目的实证数据，评估培训对收入的直接与间接效应。