Spectral adjustment for spatial confounding

Yawen Guan, Garritt L Page, Brian J Reich, Massimo Ventrucci () and Shu Yang

Biometrika, 2023, vol. 110, issue 3, 699-719

Abstract: SummaryAdjusting 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.

Keywords: Coherence; Conditional autoregressive prior; COVID-19; Matérn covariance; Spatial confounding (search for similar items in EconPapers)
Date: 2023
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