Unsupervised Causal Abstractions Discovery

2026-06-17

AI Summary

This work addresses the problem of automatically discovering high-level causal abstraction models that accurately capture interventional behaviors directly from low-level observational data, without relying on expert-specified priors. Building upon a low-rank causal discovery assumption, the authors propose an end-to-end learnable, unsupervised method that jointly integrates structural causal modeling with representation learning to identify high-level latent variables and infer their causal structure from data. The theoretical analysis establishes, for the first time, a formal connection between low-rank graph-generating mechanisms and causal abstractions, proving the identifiability and interventional validity of the learned high-level variables. Experimental results demonstrate that the proposed approach reliably recovers high-level structural causal models, offering a novel paradigm for automated causal abstraction.

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.

Problem

• causal abstractions
• structural causal model
• unsupervised learning
• low-rank causal discovery
• high-level SCM

Innovation

• causal abstraction
• low-rank causal discovery
• structural causal model
• unsupervised learning
• identifiability