| Title: | Debiased inference on heterogeneous quantile treatment effects with regression rank scores |
| Journal: | Journal of the Royal Statistical Society Series B (Statistical Methodology) |
| Published: | 8 Aug 2023 |
| DOI: | https://doi.org/10.1093/jrsssb/qkad075 |
| URL: | https://arxiv.org/pdf/2102.01753 |
Publication 10186
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Abstract
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.</p>
2 Authors
- Alexander Giessing
- Jingshen Wang
1 Application
| Application ID | Title |
|---|---|
| 48240 | Integrative analysis of UK Biobank and other genetic and genomic datasets for complex disease detection and prevention |
Enabling scientific discoveries that improve human health