ArticleJournal of biomedical informatics2022
Adjusting for indirectly measured confounding using large-scale propensity score.
Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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The trial behind it
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Who cites it
36 citing papers in PubMed.
- Observational methods for COVID-19 vaccine effectiveness research: an empirical evaluation and target trial emulation.International journal of epidemiology · 2024Trial
- Evaluating large-scale propensity score adjustment when sample size is small.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Semaglutide and Neovascular Age-Related Macular Degeneration among Adults with Type 2 Diabetes: An Observational Health Data Sciences and Informatics Network Study.Ophthalmology · 2026Observational
- Glucagon-Like Peptide-1 Receptor Agonists and Risk of Mental Health Disorders in Type 2 Diabetes: Active Comparator, New User Cohort Study.Diabetes, obesity & metabolism · 2026Article
- Active Safety Surveillance Using Real-World Evidence (ASSURE) Implementation: Transparent and Reproducible Real-World Evidence Standardized Framework to Support Safety Signal Evaluation.Pharmacoepidemiology and drug safety · 2026Article
- Heterogeneity of Treatment Effects Across Nine Glucose-Lowering Drug Classes in Type 2 Diabetes: Extension of the LEGEND-T2DM Network Study.Diabetes, obesity & metabolism · 2026Observational
- Real-world performance of large-scale propensity score adjustment strategies: Matching, weighting, and stratification.Research square · 2026Article
- Comparative Cardiovascular Effectiveness of Glucagon-Like Peptide 1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors in Diabetes Mellitus.Journal of the American College of Cardiology · 2026Article
- Comparison of Cardiovascular Events in Patients Receiving Concomitant Clopidogrel and Proton Pump Inhibitors Classified by CYP2C19 Inhibitory Potency.Arteriosclerosis, thrombosis, and vascular biology · 2026Observational
- Trust in Observational Research.Journal of the American College of Cardiology · 2026Article
- Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection.Statistics in medicine · 2026Article
- Accelerating real-world prediction and research in Alzheimer's: The M3AD study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Article
- Heterogeneity of Treatment Effects Across Nine Glucose-Lowering Drug Classes in Type 2 Diabetes: Extension of the LEGEND-T2DM Network Study.medRxiv : the preprint server for health sciences · 2026Article
- A pipeline for enabling path-specific causal fairness in observational health data.Proceedings of the ... Conference on Fairness, Accountability, and Transparency · 2026Article
- Observational
- Semaglutide and diabetic retinopathy: an OHDSI network study.BMJ open diabetes research & care · 2025 · on this mapObservational
- Large-scale Empirical Identification of Candidate Comparators for Pharmacoepidemiological Studies.Drug safety · 2025Article
- Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity.Clinical and translational science · 2025Article
- Assessing Covariate Balance With Small Sample Sizes.Statistics in medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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