A spectral adjustment for spatial confounding

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.
Original languageEnglish (US)
JournalBiometrika
DOIs
StatePublished - Dec 21 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2023-01-09
Acknowledged KAUST grant number(s): 3800.2
Acknowledgements: This work was partially supported by the NIH (R01ES031651-01,R01ES027892-01) and King Abdullah University of Science and Technology (3800.2).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)

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