Spatial Causal Inference in the Presence of Unmeasured Confounding and Interference

Oregon Convention Center 

Causal inference and spatial statistics methodology are often detached. We reconcile these threads of the literature within the realm of interference and unmeasured spatial confounding. We provide new insights on the proper analysis of spatial data sets for learning causal effects, and establish how tools from spatial statistics can be used to draw causal inferences. From a causal inference prism, we introduce spatial causal graphs to study the complications that arise when investigating causal relationships from spatial data, and we provide new insights for spatial data analysis: spatial confounding and interference can manifest as each other, and statistical dependencies in the exposure can render standard analyses invalid. We propose a parametric approach based on tools amenable to spatial statisticians that accounts for interference and mitigates bias from local and neighborhood unmeasured spatial confounding. We show that incorporating an exposure model is necessary from a Bayesian perspective. The proposed approach is based on modeling the exposure and the outcome simultaneously while accounting for the presence of common spatially-structured unmeasured predictors.