Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference

• URL: http://arxiv.org/abs/2510.08762v1
• Date: Thu, 09 Oct 2025 19:28:18 GMT
• Title: Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
• Authors: Ayush Khot, Miruna Oprescu, Maresa Schröder, Ai Kagawa, Xihaier Luo,
• Abstract summary: Causal inference in spatial domains faces two intertwined challenges: unmeasured spatial factors, and interference from nearby treatments.<n>We propose the Spatial Deconfounder, a two-stage method that reconstructs a substitute confounder from local treatment vectors.<n>We show that this approach enables nonparametric identification of both direct and effects under weak assumptions.
• Score: 9.753644414327225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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