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Unsupervised Causal Abstractions Discovery
[Submitted on 17 Jun 2026] · 2026-06-19 · via cs.LG updates on arXiv.org

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Abstract:Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM. Existing applications of this notion largely follow a hypothesis-testing paradigm: an expert proposes a candidate high-level model and then evaluates if the low-level system implements it. We study the complementary problem of learning a high-level model directly from low-level measurements. Our contributions leverage hypotheses from low-rank causal discovery, and can be summarized as follows: (1) we show that observations generated by a low-rank graph induce latents that form a causal abstraction, (2) we provide identifiability results about these latents, and (3) we propose a practical objective to learn this high-level SCM.

Submission history

From: Théo Saulus [view email]
[v1] Wed, 17 Jun 2026 20:56:44 UTC (95 KB)

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