Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions
针对重尾分布结果变量，提出一种估计超出数据范围的极端分位数处理效应的方法，并给出渐近正态性和方差估计，应用于大学教育对工资的极端影响分析。
Causal inference for extreme events has many potential applications in fields such as climate science, medicine, and economics. We study the extremal quantile treatment effect of a binary treatment on a continuous, heavy-tailed outcome. Existing methods are limited to the case where the quantile of interest is within the range of the observations. For applications in risk assessment, however, the most relevant cases relate to extremal quantiles that go beyond the data range. We introduce an estimator of the extremal quantile treatment effect that relies on asymptotic tail approximation, and use a new causal Hill estimator for the extreme value indices of potential outcome distributions. We establish asymptotic normality of the estimators and propose a consistent variance estimator to achieve valid statistical inference. We illustrate the performance of our method in simulation studies, and apply it to a real dataset to estimate the extremal quantile treatment effect of college education on wage. Supplementary materials for this article are available online.