A proxy-based approach for unmeasured confounding in electronic health records research

2025/06/13 by Haley Colgate Kottler, Kottler, Haley Colgate, Amy L. Cochran +1
Medicine · #Advanced MRI Techniques and Applications #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #Methodology (stat.ME) #Quantitative Methods (q-bio.QM) #Traditional Chinese Medicine Studies

paper · pdf · doi:10.48550/arxiv.2506.12177

openalex publication_date 2025/06/13 · openalex created_date 2025/10/13 · openalex updated_date 2026/07/29

Abstract

Electronic health records (EHR) are widely used to study clinical decisions, yet unmeasured confounding remains a persistent challenge. Proxy variables offer a potential solution. In EHR data, clinicians already record many such measurements (e.g., vitals), each revealing something about a patient's underlying health. Despite this, proxy-based methods are rarely used in practice. We introduce a new way to use proxies to adjust for unmeasured confounding. Our approach uses a vector of proxies to construct covariates that capture aspects of the unmeasured confounder, which are then included in a regression model. As one implementation, we use factor analysis followed by regression. We compare this approach with existing methods, including proximal causal inference, across a range of realistic settings. In practice, assumptions rarely hold exactly, so we study what happens when models are misspecified and variables are used incorrectly: e.g., a confounder or instrument is treated as a proxy. Finally, we apply the method to EHR data to estimate the effect of hospital admission for older adults presenting to the emergency department with chest pain, a setting where unmeasured confounding is a substantial concern. This work provides a practical way to use proxies and may help bring proxy-based methods into broader use.

Citations

• Regression-Based Proximal Causal Inference
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