ArticleBiostatistics (Oxford, England)2019
Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching.
Article in Biostatistics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Differential associations of active and passive digital modalities with health perception biases among older adults.BMC geriatrics · 2026Article
- Mitigating the risk of bias exacerbation when controlling for unmeasured spatial confounding for binary exposures.American journal of epidemiology · 2026Article
- Article
- Authors' reply to the Discussion of 'Methods for estimating the exposure-response curve to inform the new safety standards for fine particulate matter'.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Consistency of common spatial estimators under spatial confounding.Biometrika · 2025Article
- Alleviating spatial confounding in frailty models.Biostatistics (Oxford, England) · 2023Article
- Generalized propensity score approach to causal inference with spatial interference.Biometrics · 2023Article
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.International statistical review = Revue internationale de statistique · 2021Article
- Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies.International journal of health geographics · 2021Article
- Bipartite Causal Inference with Interference.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.Statistics in medicine · 2020Article
- Selecting a Scale for Spatial Confounding Adjustment.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2020Article
- Causal inference with interfering units for cluster and population level treatment allocation programs.Biometrics · 2019Article
- Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes.Statistical methods in medical research · 2019Article
- Article
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Authors and funding
3 authors.
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Abstract
Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed distance adjusted propensity score matching (DAPSm) that incorporates information on units' spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and some forms of unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a "standard" propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade-offs of prioritizing observed confounding adjustment versus spatial proximity adjustment.
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