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 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.

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，我们专注于学习有向无环图和各种概括的方法，这些方法允许在可用数据中观察到某些变量。我们特别关注因果结构学习的两个基本组合方面。首先，我们讨论因果图搜索空间的结构。其次，我们讨论因果图上的等价类的结构，即表示仅从观察数据中可以学到什么的图集，以及如何通过添加干预数据来细化这些等价类。