论文标题
因果结构学习：组合观点
Causal Structure Learning: a Combinatorial Perspective
论文作者
论文摘要
在这篇综述中，我们讨论了从数据中学习因果结构的方法，也称为因果发现。特别是，我们重点介绍学习定向的无环图（DAG）和各种概括的方法，这些方法允许在可用数据中观察到某些变量。我们将特殊的关注对因果结构学习的两个基本组合方面。首先，我们在因果图上讨论搜索空间的结构。其次，我们讨论了在因果图上的等价类别的结构，即仅从观察数据中学到的图表集，以及如何通过添加介入的数据来完善这些等效类别。
In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on approaches for learning directed acyclic graphs (DAGs) and various generalizations which allow for some variables to be unobserved in the available data. We devote special attention to two fundamental combinatorial aspects of causal structure learning. First, we discuss the structure of the search space over causal graphs. Second, we discuss the structure of equivalence classes over causal graphs, i.e., sets of graphs which represent what can be learned from observational data alone, and how these equivalence classes can be refined by adding interventional data.
