arXiv：cs.LG· Gaetano Tedesco (University of Amsterdam), Alex Markham (University of Copenhagen)·· 4 小时前
COARSE：基于分数学习的干预数据聚类 DAG 方法
Score-Based Learning of Cluster DAGs from Interventions
AI 导读
研究人员提出 COARSE，这是首个基于分数的方法，用于从干预数据中学习聚类 DAG。在���性高斯假设下，它将约束型边学习阶段替换为基于分数的阶段，并证明干预本身可识别聚类间的因果序，将边学习化简为每个聚类的一次局部搜索。该方法具有多项式时间复杂度，在合成和真实干预数据上边恢复效果匹配 SOTA，边学习阶段速度最高快两个数量级。
正文
Abstract:Graphical approaches to causal abstraction transform a low-level causal directed acyclic graph (DAG) over many measured variables into a smaller, high-level DAG whose nodes cluster the original variables and whose edges summarize the causal relations between clusters. Such cluster DAGs are easier to interpret, but learning them requires finding the clusters and recovering the edges between them. Madaleno et al. (2026) learn the interventional coarsening (the cluster DAG that merges variables the interventions cannot distinguish) in two constraint-based phases: first the clusters, then the edges. We introduce COARSE, the first score-based method for this task: it keeps the two-phase structure but, under linear Gaussian assumptions, swaps the constraint-based edge phase for a score-based one. We show that the interventions themselves identify a causal order over the clusters, and learning the edges reduces to a single local search per cluster under a cluster-level BIC score. We prove that the procedure runs in polynomial time and, provided the variables affected by each intervention are correctly identified, that it is consistent. On synthetic and real-world interventional data, COARSE matches state-of-the-art edge recovery given enough samples, with an edge phase up to two orders of magnitude faster, including on dense graphs with hundreds of nodes.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11947 [stat.ML] |
| (or arXiv:2610.11947v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11947 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gaetano Tedesco [view email]
[v1]
Thu, 8 Oct 2026 13:35:04 UTC (400 KB)
来源：arXiv：cs.LG · arxiv.org