arXiv Machine Learning By Gerrit Gro{\ss}mann, Sumantrak Mukherjee, Sebastian J. Vollmer

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

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arXiv:2608. 08064v1 Announce Type: new Abstract: Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated interventions and outcomes.

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arXiv Machine Learning
Jun 30

Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference

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.

By Ayush Khot, Miruna Oprescu, Maresa Schr\"oder, Ai Kagawa, Xihaier Luo
arXiv Machine Learning
Sep 22

Causal Inference with Unobserved Confounding: A Mixture Learning Perspective

The paper introduces a mixture‑learning framework for causal inference with unobserved confounding, treating latent confounders as sources of heterogeneity that create mixture structures in observed data. By assuming suitable structural and identifiability conditions, it shows that recovering the mixing distribution and component mechanisms allows estimation of interventional distributions and causal estimands. The authors illustrate the approach with Bernoulli mixture examples, extend it to high‑dimensional exponential‑family mixtures with dependent outcomes, and relate it to panel‑data settings, latent factor models, and synthetic interventions.

By Mansi Sood, Devavrat Shah