Unsupervised Causal Abstractions Discovery

• URL: http://arxiv.org/abs/2606.19594v1
• Date: Wed, 17 Jun 2026 20:56:44 GMT
• Title: Unsupervised Causal Abstractions Discovery
• Abstract summary: Causal abstractions formalize when a high-level structural causal model captures the interventional behavior of a lower-level SCM.<n>We study the complementary problem of learning a high-level model directly from low-level measurements.
• Score: 14.450590098991329
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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