Spatial causal inference in the presence of unmeasured confounding and interference

Abstract

Causal inference in spatial settings is met with unique challenges and opportunities. On one hand, a unit's outcome can be affected by the exposure at many locations, leading to interference. On the other hand, unmeasured spatial variables can confound the effect of interest. Our work has two overarching goals. First, using causal diagrams, we illustrate that spatial confounding and interference can manifest as each other, meaning that investigating the presence of one can lead to wrongful conclusions in the presence of the other, and that statistical dependencies in the exposure variable can render standard analyses invalid. This can have crucial implications for analyzing data with spatial or other dependencies, and for understanding the effect of interventions on dependent units. Secondly, we propose a parametric approach to mitigate bias from local and neighborhood unmeasured spatial confounding and account for interference simultaneously. This approach is based on simultaneous modeling of the exposure and the outcome while accounting for the presence of spatially-structured unmeasured predictors of both variables. We illustrate our approach with a simulation study and with an analysis of the local and interference effects of sulfur dioxide emissions from power plants on cardiovascular mortality.

Lung Cancer Prevalence in Virginia: A Spatial Zipcode-Level Analysis via INLA

“…While our study incorporates smoking indices as a covariate, it is crucial to note that the observational nature of the study restricts us from drawing definitive conclusions regarding any causal relationship between smoking and LC. In order to assess smoking–lung cancer causality, it would be necessary to use a spatial causal inferential framework while mitigating the challenges of overdispersion and missing covariate information (such as a spatial–causal framework in the presence of unmeasured confounders [ 56 ], which can arise due completely missing/unobserved/mismeasured covariates or an incorrect functional form in the outcome model. Moreover, smoking indices do not consider the situation in which measured covariates have missing information.…”