Debiased inference on heterogeneous quantile treatment effects with regression rank scores

• 成果类型: Article
• 署名作者: Giessing, Alexander; Wang, Jingshen
• 署名单位: University of Washington; University of Washington Seattle; University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkad075
• 发表日期: 2024
• 页码: 1561-1588
• 关键词: efficient semiparametric estimation; MODEL; RISK; selection; balance

摘要

Understanding treatment effect heterogeneity is vital to many scientific fields because the same treatment may affect different individuals differently. Quantile regression provides a natural framework for modelling such heterogeneity. We propose a new method for inference on heterogeneous quantile treatment effects (HQTE) in the presence of high-dimensional covariates. Our estimator combines an l(1)-penalised regression adjustment with a quantile-specific bias correction scheme based on rank scores. We study the theoretical properties of this estimator, including weak convergence and semi-parametric efficiency of the estimated HQTE process. We illustrate the finite-sample performance of our approach through simulations and an empirical example, dealing with the differential effect of statin usage for lowering low-density lipoprotein cholesterol levels for the Alzheimer's disease patients who participated in the UK Biobank study.