Observational PFAS Studies and Spatial Causal Inference Methods

Music City Center 

Unmeasured spatial confounding complicates exposure effect estimation in environmental health studies. This problem is exacerbated in studies with multiple health outcomes and environmental exposure variables, as the source and magnitude of confounding bias may differ across exposure/outcome pairs. We propose to mitigate the effects of spatial confounding in multivariate studies by projecting to the spectral domain to separate relationships by the spatial scale, and assuming that the confounding bias dissipates at more local scales. Our model for the exposure effects is a three-way tensor over exposure, outcome and spatial scale. We use a canonical polyadic decomposition and shrinkage priors to encourage sparsity and borrow strength across the dimensions of the tensor. We demonstrate the performance of our method in an extensive simulation study and data analysis about perfluoroalkyl and polyfluoroalkyl substances (PFAS) and several health outcomes.

Causal inference

Mixtures

PFAS

Tensor decomposition