Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
This work addresses robust causal inference in structured spatial data for fields like environmental health and social science, bridging spatial and deconfounding literatures.
The paper tackles the intertwined challenges of unmeasured spatial confounders and treatment interference in spatial causal inference by proposing the Spatial Deconfounder, a two-stage method that reconstructs a substitute confounder from local treatment vectors using a CVAE with a spatial prior and estimates causal effects via a flexible outcome model, showing consistent improvements in effect estimation across real-world datasets in environmental health and social science.
Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outcome, and (2) interference from nearby treatments that violate standard no-interference assumptions. While existing methods typically address one by assuming away the other, we show they are deeply connected: interference reveals structure in the latent confounder. Leveraging this insight, we propose the Spatial Deconfounder, a two-stage method that reconstructs a substitute confounder from local treatment vectors using a conditional variational autoencoder (CVAE) with a spatial prior, then estimates causal effects via a flexible outcome model. We show that this approach enables nonparametric identification of both direct and spillover effects under weak assumptions--without requiring multiple treatment types or a known model of the latent field. Empirically, we extend SpaCE, a benchmark suite for spatial confounding, to include treatment interference, and show that the Spatial Deconfounder consistently improves effect estimation across real-world datasets in environmental health and social science. By turning interference into a multi-cause signal, our framework bridges spatial and deconfounding literatures to advance robust causal inference in structured data.