Robust and Agnostic Learning of Conditional Distributional Treatment Effects
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条件分布处理效应的稳健与模型无关学习
- 研究问题
- 当条件平均处理效应无法体现尾部风险时，如何稳健学习条件分位数、超分位数及其他分布处理效应，并允许灵活回归模型？
- 方法与思路
- 作者构造特殊伪结果，再用任意回归学习器对协变量回归，目标涵盖条件分位数、超分位数与由 f 散度定义的相干风险度量效应。
- 材料陈述的贡献
- 摘要称方法可学习条件分布效应在模型类中的最佳投影，允许干扰函数较慢收敛，并对线性投影进行推断，辅以模拟和财富案例。
- 阅读关注点
- 应核对因果识别条件、伪结果的构造与稳健性要求，以及干扰函数速率、模型类复杂度和线性投影推断之间的限制。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明具体速率和识别条件，模型无关不意味着可在任意数据条件下识别效应。
原文摘要
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.