Adjusting for indirectly measured confounding using large-scale propensity score

Linying Zhang, Yixin Wang, Martijn J. Schuemie, David M. Blei, George Hripcsak

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Confounding remains one of the major challenges to causal inference with observational data. This problem is paramount in medicine, where we would like to answer causal questions from large observational datasets like electronic health records (EHRs) and administrative claims. Modern medical data typically contain tens of thousands of covariates. Such a large set carries hope that many of the confounders are directly measured, and further hope that others are indirectly measured through their correlation with measured covariates. How can we exploit these large sets of covariates for causal inference? To help answer this question, this paper examines the performance of the large-scale propensity score (LSPS) approach on causal analysis of medical data. We demonstrate that LSPS may adjust for indirectly measured confounders by including tens of thousands of covariates that may be correlated with them. We present conditions under which LSPS removes bias due to indirectly measured confounders, and we show that LSPS may avoid bias when inadvertently adjusting for variables (like colliders) that otherwise can induce bias. We demonstrate the performance of LSPS with both simulated medical data and real medical data.

Original languageEnglish
Article number104204
JournalJournal of Biomedical Informatics
Volume134
DOIs
StatePublished - Oct 2022

Keywords

  • Causal inference
  • Electronic health record
  • Observational study
  • Propensity score
  • Unmeasured confounder

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