提出高效算法，从复杂图中发现因果关系。
An efficient search-and-score algorithm for ancestral graphs using multivariate information scores
- 基于多变量信息得分，分两步局部搜索优化图结构。
- 在含隐变量的复杂数据集上，优于现有顶尖方法。
- 适合需要快速准确推断因果结构的研究者。
我们提出一种贪心搜索与评分算法，用于处理包含有向边和双向边（源于未观测潜变量）的祖先图。通过计算相关顶点集C的多变量信息得分来估计祖先图的归一化似然分数，其中C为通过限定于祖先集内的碰撞路径相连的ac-连通子集。为提升计算效率，所提双阶段算法分别基于每个节点邻近区域（第1步）及每条边周围局部信息（第2步）进行评估。尽管仅考虑最多含两条碰撞路径的ac-连通子集，该方法在具有挑战性的基准数据集上仍显著优于当前最优因果发现方法。
原文摘要 · Abstract (English)
We propose a greedy search-and-score algorithm for ancestral graphs, which include directed as well as bidirected edges, originating from unobserved latent variables. The normalized likelihood score of ancestral graphs is estimated in terms of multivariate information over relevant ``ac-connected subsets'' of vertices, C, that are connected through collider paths confined to the ancestor set of C. For computational efficiency, the proposed two-step algorithm relies on local information scores limited to the close surrounding vertices of each node (step 1) and edge (step 2). This computational strategy, although restricted to information contributions from ac-connected subsets containing up to two-collider paths, is shown to outperform state-of-the-art causal discovery methods on challenging benchmark datasets.
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