从底层数据自动发现高层次因果抽象，无需人工假设。
Unsupervised Causal Abstractions Discovery
- 基于低秩因果图生成的观测数据，可推导出构成因果抽象的潜在变量。
- 证明了这些潜在变量在特定条件下具有唯一性（可辨识性）。
- 提出可落地的学习目标，适用于无监督场景下的高阶因果建模。
因果抽象形式化了高层结构因果模型（SCM）如何捕捉底层SCM的干预行为。现有应用多采用假设检验范式：专家提出候选高层模型，再验证底层系统是否实现该模型。本文研究从底层测量数据直接学习高层模型的互补问题。贡献包括：(1) 证明由低秩图生成的观测数据会诱导出形成因果抽象的潜在变量；(2) 提供这些潜在变量的可辨识性结果；(3) 提出一个实用的目标函数以学习该高层SCM。
原文摘要 · Abstract (English)
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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