arXiv Machine Learning June 30, 2026 By Ayush Khot, Miruna Oprescu, Maresa Schr\"oder, Ai Kagawa, Xihaier Luo

Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference

Read the original on arXiv Machine Learning →

arXiv:2510. 08762v2 Announce Type: replace 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

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CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data

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arXiv Machine Learning

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Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference

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llms

arXiv Machine Learning

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arXiv Machine Learning

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multimodal safety

arXiv Machine Learning

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By Humaira Anzum, Md Ishtyaq Mahmud, Jagan Mohan Reddy Dwarampudi, Tania Banerjee

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CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data

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