Spatial causal inference in the presence of sampling bias

Oregon Convention Center 

Environmental data are often observational and exhibit spatial dependence, making causal effects of treatments or policies difficult to estimate. Unmeasured spatial confounders, i.e., spatial processes correlated with both the treatment assignment mechanism and the outcome, can introduce bias when estimating causal effects of interest since important assumptions in causal inference are violated. Spatial data can also be subject to preferential sampling, where sampling of locations are related to unmeasured confounders or the response variable, which introduces additional bias to the estimation of model parameters. We propose a spatial causal inference method that simultaneously accounts for unmeasured spatial confounders in both sampling locations and treatment allocation. We prove the identifiability of key parameters in the model and the consistency of the posterior distributions of those parameters. We also show via simulation studies that the causal effect of interest can be reliably estimated under the proposed model. The proposed method is applied to assess the effect of policies that govern marine protected areas on fish biodiversity.

Poisson process

Preferential sampling

Spatial confounding

Potential outcomes 

Section on Statistics and the Environment