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Activity Number:
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381
- Recent Advances in High-Dimensional Estimation and Inference Methods
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #323218
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Title:
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Debiased Inference on Heterogeneous Quantile Treatment Effects with Regression Rank-Scores
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Author(s):
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Alexander Giessing* and Jingshen Wang
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Companies:
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University of Washington and UC Berkeley
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Keywords:
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Quantile Regression;
Debiased Inference;
High-dimensional Data;
Causal Inference;
Neyman Orthogonalization;
Semi-Parametric Efficiency
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Abstract:
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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. In this paper, we propose a new method for inference on heterogeneous quantile treatment effects in the presence of high-dimensional covariates. Our estimator combines a 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 and its relation to (near) Neyman orthogonalization. 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.
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Authors who are presenting talks have a * after their name.
