ArticleBiometrika2023
Spectral adjustment for spatial confounding.
Article in Biometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Article
- Semiparametric approaches for mitigating spatial confounding in large environmental epidemiology cohort studies.Environmetrics · 2025Article
- Article
- Consistency of common spatial estimators under spatial confounding.Biometrika · 2025Article
- Estimating spatially varying health effects of wildland fire smoke using mobile health data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Discussion on "Spatial+: A novel approach to spatial confounding" by Dupont, Wood, and Augustin.Biometrics · 2022Article
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.International statistical review = Revue internationale de statistique · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the exposure to the mean of the response variable. Within this general framework, we propose a sequence of confounder adjustment methods that range from parametric adjustments based on the Matérn coherence function to more robust semiparametric methods that use smoothing splines. These ideas are applied to areal and geostatistical data for both simulated and real datasets.
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Registered trials
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