Debiased inference on heterogeneous quantile treatment effects with regression rank scores
提出一种结合L1惩罚回归调整与分位数特定秩得分偏差校正的新方法，用于高维协变量下异质性分位数处理效应的推断，并通过模拟和英国生物银行数据中他汀类药物对阿尔茨海默病患者低密度脂蛋白胆固醇的差异化效应进行验证。
Abstract 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 ℓ1-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.