A robust and efficient approach to causal inference based on sparse sufficient dimension reduction

DOI10.1214/18-AOS1722zbMath1420.62178OpenAlexW2911510395WikidataQ64898186 ScholiaQ64898186MaRDI QIDQ2414095

Zhiwei Zhang, Li-ping Zhu, Shujie Ma, Raymond J. Carroll, Chih-Ling Tsai

Publication date: 10 May 2019

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aos/1550026847

zbMATH Keywords

dimension reductionhigh-dimensional datasemiparametric efficiencyaverage treatment effectmultiple-index modeloutcome regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

A beyond multiple robust approach for missing response problem ⋮ Model averaging assisted sufficient dimension reduction ⋮ Recent advances in statistical methodologies in evaluating program for high-dimensional data ⋮ Sufficient dimension reduction for average causal effect estimation ⋮ Outcome regression-based estimation of conditional average treatment effect ⋮ Statistical inference of heterogeneous treatment effect based on single-index model ⋮ Missing data analysis with sufficient dimension reduction ⋮ Neighborhood-based cross fitting approach to treatment effects with high-dimensional data ⋮ Unnamed Item ⋮ On IPW-based estimation of conditional average treatment effects