High-dimensional confounding adjustment in causal inference

Sanghun Cha (), Joon Jin Song () and Kyeong Eun Lee ()
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Sanghun Cha: Kyungpook National University
Joon Jin Song: Baylor University
Kyeong Eun Lee: Kyungpook National University

AStA Advances in Statistical Analysis, 2025, vol. 109, issue 3, No 4, 463-481

Abstract: Abstract When estimating treatment effects in observational studies, propensity score analysis (PSA) is commonly used to reduce the arising bias that results from confounders interfering with causal inference. However, propensity score (PS) estimation is unstable if some confounders are densely measured and formed into high-dimensional data, which could eventually result in a biased estimate of the treatment effect. We propose two-stage analytic procedures to mitigate the high-dimensional problem: ridge PSA and functional PSA. In addition, conventional variance estimation of treatment effect estimates in the PSA methods tends to be biased, so we leverage the empirical bootstrap approach to develop a valid variance estimator. In the simulation study, we compare the bias and MSE of treatment effects estimated by ridge PSA and function PSA under the various confounding structures, including more densely measured confounders, and evaluate the performance of bootstrap variance estimators. The proposed methods are applied in the case study of police shootings.

Keywords: Bootstrap; Causal inference; Functional confounding; High-dimensional data; Ridge regression (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10182-025-00528-3

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