Unsupervised Causal Abstractions Discovery
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Computer Science > Machine Learning
Title:Unsupervised Causal Abstractions Discovery
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.19594 [cs.LG] |
| (or arXiv:2606.19594v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19594
arXiv-issued DOI via DataCite (pending registration)
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