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arXiv Machine Learning By Mansi Sood, Devavrat Shah

Causal Inference with Unobserved Confounding: A Mixture Learning Perspective

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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