The conditional average treatment effect (CATE) is the best measure of individual causal effects given baseline covariates. However, the CATE only captures the (conditional) average, and can overlook risks and tail events, which are important to treatment choice. In aggregate analyses, this is usually addressed by measuring the distributional treatment effect (DTE), such as differences in quantiles or tail expectations between treatment groups. Hypothetically, one can similarly fit 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 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 covariates using any regression learner. Our method is model-agnostic in that it can provide the best projection of CDTE onto the regression model class. Our method is robust in 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 behavior of our proposal in simulations, as well as in a case study of 401(k) eligibility effects on wealth.
翻译:有条件平均治疗效果(CATE)是衡量每个治疗组之间按基准变量计算的个人因果效应的最佳尺度。 但是, CATE只能捕捉(有条件)平均值,并且可以忽略风险和尾端事件,这对于治疗选择很重要。 在总体分析中,通常通过测量分布式治疗效果(DTE)来解决这个问题,例如四分位数的差异或治疗组之间的尾端预期。假设,人们可以同样地适应每个治疗组的有条件微分回归,并取其差异,但是这不会强到错误区分,或者提供类内最佳预测。我们为学习有条件的DTE(CDTE)提供了一种新的稳健和示范性资格方法,用于学习有条件的DTE(CDTE),这一系列问题包括有条件的四分位数治疗效果、有条件的超量治疗效果,以及治疗组群之间对一致风险措施的附带影响。 我们的方法是以任何回归学习者构建一种特殊的伪收益和递增变数为基础。 我们的方法是模型,我们甚至可以模拟40分级的模型,我们甚至可以用来在排序中进行最稳的递增的 CDTE 模型,我们可以在学习这些 CDTE 的回归率的模型中进行最稳的模型,我们学习。</s>