Proposes a spatial causal inference model jointly handling preferential sampling and treatment allocation with unmeasured confounders, proves identifiability and posterior consistency, validates via simulations, and applies to Australian MPA data showing bias affects effect estimates.
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Spatial causal inference in the presence of preferential sampling to study the impacts of marine protected areas
Proposes a spatial causal inference model jointly handling preferential sampling and treatment allocation with unmeasured confounders, proves identifiability and posterior consistency, validates via simulations, and applies to Australian MPA data showing bias affects effect estimates.