Spatial causal inference in the presence of unmeasured confounding and interferenceCausal 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.
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