The conditional average treatment effect (CATE) is the best point prediction of individual causal effects given individual baseline covariates and can help personalize treatments. However, as CATE only reflects the (conditional) average, it can wash out potential risks and tail events, which are crucially relevant to treatment choice. In aggregate analyses, this is usually addressed by measuring distributional treatment effect (DTE), such as differences in quantiles or tail expectations between treatment groups. Hypothetically, one can similarly fit covariate-conditional quantile regressions in each treatment group and take their difference, but this would not be robust to misspecification or provide agnostic best-in-class predictions. We provide a new robust and model-agnostic methodology for learning the conditional DTE (CDTE) for a wide class of problems that includes conditional quantile treatment effects, conditional super-quantile treatment effects, and conditional treatment effects on coherent risk measures given by $f$-divergences. Our method is based on constructing a special pseudo-outcome and regressing it on baseline covariates using any given regression learner. Our method is model-agnostic in the sense that it can provide the best projection of CDTE onto the regression model class. Our method is robust in the sense that even if we learn these nuisances nonparametrically at very slow rates, we can still learn CDTEs at rates that depend on the class complexity and even conduct inferences on linear projections of CDTEs. We investigate the performance of our proposal in simulation studies, and we demonstrate its use in a case study of 401(k) eligibility effects on wealth.

翻译：有条件平均治疗效应(CATE)是针对个人基准共变数的个体因果效应的最佳预测点,可以帮助个人化治疗。然而,由于CATE仅反映(有条件)平均值,它可以冲洗潜在风险和尾端事件,这些风险和尾端事件与治疗选择密切相关。在总体分析中,通常通过测量分布治疗效应(DTE)来解决,例如四分位数的差异或治疗组之间的尾端预期。假设,人们同样可以适应每个治疗组40个的共变质质微量回归,并取其差异,但是,由于CATE仅反映(有条件)平均值,这不会强到错误或提供直线性类最佳预测。我们为学习条件性DTE(CDTE)提供了一种新的稳健和模式方法,我们用回归率的模型学习了这种方法。我们学习了最稳健的 CD(CD) 方法可以提供最稳健的学习速度。我们在排序模型中学习了这种方法。