Patrick Schnell

• 
  Mitigating Bias from Unobserved Spatial Confounders Using Mixed Effects Models
  Patrick Schnell
  Confounding by unmeasured spatial variables has been a recent interest in both spatial statistics and causal inference literature, but the concepts and approaches have remained largely separated. We aim to add a link between these branches of statistics by considering unmeasured spatial confounding within a formal causal inference framework, and estimating effects using outcome regression tools popular within the spatial statistics literature. We show that the common approach of using spatially correlated random effects does not mitigate bias due to spatial confounding, and present a set of assumptions that can be used to do so. Based on these assumptions and a conditional autoregressive model for spatial random effects, we propose an affine estimator which addresses this deficiency, and illustrate its application to causes of fine particulate matter concentration in New England.