Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions

• 成果类型: Article
• 署名作者: Deuber, David; Li, Jinzhou; Engelke, Sebastian; Maathuis, Marloes H.
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Geneva; Stanford University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2252141
• 发表日期: 2024
• 页码: 2206-2216
• 关键词: propensity score; returns; models; attribution; education; selection; MARKET

摘要

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.