Spatial Causal Inference with Preferential Sampling

Oregon Convention Center 

Environmental data are often observational and spatially dependent, making casual treatment effects difficult to estimate. Unmeasured spatial confounders, i.e., spatial variables correlated with both the treatment and response, can induce bias and invalidate inference. Spatial data can also be subject to preferential sampling, where the locations of the samples are driven by unmeasured covariates or even the assumed value of the response of interest. We propose a method that simultaneously accounts for unmeasured confounders in both the sampling locations and treatment allocation. We prove that the key parameters in the model are identifiable and show via simulations that the causal effect of interest can be reliably estimated under the assumed model. The proposed method is applied to study the effect of marine protection areas on fish biodiversity.