Title: Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
Primary area: causal reasoning
Keywords: causal inference, spatial data, unobserved confounding, interference, spillover effects, observational studies

Abstract: 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.

TL;DR: We use spatial interference as signal to deconfound spatial causal effects.