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A0606
Title: Debiased inference on heterogeneous quantile treatment effects with regression rank-scores Authors:  Alexander Giessing - University of Washington (United States) [presenting]
Jingshen Wang - University of Michigan (United States)
Abstract: Understanding treatment effect heterogeneity in observational studies is of great practical importance to many scientific fields. Quantile regression provides a natural framework for modeling such heterogeneity. We propose a new method for inference on heterogeneous quantile treatment effects in the presence of high-dimensional covariates. Our estimator combines an L1-penalized regression adjustment with a quantile-specific bias correction scheme based on quantile regression rank scores. We present a comprehensive study of the theoretical properties of this estimator, including weak convergence of the heterogeneous quantile treatment effect process to a Gaussian process. We illustrate the finite-sample performance of our approach through Monte Carlo experiments 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.