标题： 基于机器学习的直接和间接分位数处理效应的双重稳健估计 显示英文标题

标题： Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning

摘要： 我们建议在选择可观测变量假设下，使用双重/去偏机器学习估计器来估计直接和间接分位数处理效应。 这使得能够将特定结果排名上的二元处理的因果效应分解为通过称为中介变量的中间变量起作用的间接成分和（未中介的）直接影响。 所提出的方法基于潜在结果的累积分布函数的有效得分函数，对某些干扰参数（即结果、处理和中介模型）的错误设定具有鲁棒性。 我们通过机器学习估计这些干扰参数，并使用交叉拟合来减少直接和间接分位数处理效应估计中的过拟合偏差。 我们建立了效应估计量的一致性和渐近正态性。 我们还提出了一个乘子Bootstrap进行统计推断，并证明了乘子Bootstrap的有效性。 最后，我们在模拟研究中调查了我们方法的有限样本性能，并将其应用于国家职业公司研究的实证数据，以评估培训对收入的直接和间接影响。 显示英文摘要

摘要： 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.