[2202.00814] Adjustment for Unmeasured Spatial Confounding in Settings of Continuous Exposure Conditional on the Binary Exposure Status: Conditional Generalized Propensity Score-Based Spatial Matching Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2202.00814 (stat) [Submitted on 1 Feb 2022 ( v1 ), last revised 30 Apr 2026 (this version, v4)] Title: Adjustment for Unmeasured Spatial Confounding in Settings of Continuous Exposure Conditional on the Binary Exposure Status: Conditional Generalized Propensity Score-Based Spatial Matching Authors: Honghyok Kim , Michelle Bell View a PDF of the paper titled Adjustment for Unmeasured Spatial Confounding in Settings of Continuous Exposure Conditional on the Binary Exposure Status: Conditional Generalized Propensity Score-Based Spatial Matching, by Honghyok Kim and 1 other authors View PDF Abstract: Propensity score (PS) matching to estimate causal effects of exposure is biased when unmeasured spatial confounding exists. Some exposures are continuous yet dependent on a binary variable (e.g., level of a contaminant (continuous) within a specified radius from residence (binary)). Further, unmeasured spatial confounding may vary by spatial patterns for both continuous and binary attributes of exposure. We propose a new generalized propensity score (GPS) matching method for such settings, referred to as conditional GPS (CGPS)-based spatial matching (CGPSsm). A motivating example is to investigate the association between proximity to refineries with high petroleum production and refining (PPR) and stroke prevalence in the southeastern United States. CGPSsm matches exposed observational units (e.g., exposed participants) to unexposed units by their spatial proximity and GPS integrated with spatial information. GPS is estimated by separately estimating PS for the binary status (exposed vs. unexposed) and CGPS on the binary status. CGPSsm maintains the salient benefits of PS matching and spatial analysis: straightforward assessments of covariate balance and adjustment for unmeasured spatial confounding. Simulations showed that CGPSsm can adjust for unmeasured spatial confounding. Using our example, we found positive association between PPR and stroke prevalence. Our R package, CGPSspatialmatch, has been made publicly available. Comments: Online supplementary materials are appended at the bottom of the main pdf As of 2026, under revision at a method-oriented journal Subjects: Methodology (stat.ME) ; Applications (stat.AP) Cite as: arXiv:2202.00814 [stat.ME] (or arXiv:2202.00814v4 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2202.00814 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Honghyok Kim [ view email ] [v1] Tue, 1 Feb 2022 23:54:00 UTC (7,143 KB) [v2] Fri, 4 Feb 2022 21:50:12 UTC (8,005 KB) [v3] Wed, 16 Mar 2022 03:10:49 UTC (7,871 KB) [v4] Thu, 30 Apr 2026 21:47:55 UTC (7,871 KB) Full-text links: Access Paper: View a PDF of the paper titled Adjustment for Unmeasured Spatial Confounding in Settings of Continuous Exposure Conditional on the Binary Exposure Status: Conditional Generalized Propensity Score-Based Spatial Matching, by Honghyok Kim and 1 other authors View PDF view license Current browse context: stat.ME < prev | next > new | recent | 2022-02 Change to browse by: stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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