Causal Inference for Heterogeneous Extreme Quantiles with Heavy-Tailed Outcomes

2026-09-03

AI Summary

本文提出一种估计重尾结果条件下极端分位数处理效应的方法，结合逆概率加权分位数回归和极值理论进行估计。

Abstract

We propose a framework for estimating conditional extreme quantile treatment effects (CEQTEs) in observational studies with heavy-tailed outcomes. Our procedure first estimates intermediate conditional quantiles using inverse-probability-weighted (IPW) quantile regression and then extrapolates them to extreme levels using extreme value theory. Under a linear conditional quantile model, we show that the conditional and marginal distributions of each potential outcome share a common extreme value index (EVI), motivating two complementary Hill-type EVI estimators based on conditional and marginal information, respectively. On the theoretical front, we introduce an IPW tail quantile score process that bridges regression quantile score processes and uniform tail empirical processes while accounting for treatment assignment. We establish its functional weak convergence under mild regularity conditions, without requiring a max-domain-of-attraction condition. This result provides the probabilistic foundation for the asymptotic analysis of the proposed CEQTE estimators. Simulation studies demonstrate favorable finite-sample performance, and an application to NLSY79 data reveals substantial heterogeneity in the effect of college education on extremely high hourly wages across confounder-defined subpopulations.

Problem

• Causal Inference
• Extreme Quantiles
• Heavy-Tailed Outcomes
• Conditional Extreme Quantile Treatment Effects
• Observational Studies

Innovation

• Conditional Extreme Quantile Treatment Effects (CEQTEs)
• Inverse-Probability-Weighted (IPW) Quantile Regression
• Extreme Value Theory
• IPW Tail Quantile Score Process
• Heavy-Tailed Outcomes