Squires Chandler, Uhler Caroline
Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Broad Institute and Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Found Comut Math. 2022 Aug 1:1-35. doi: 10.1007/s10208-022-09581-9.
In this review, we discuss approaches for learning causal structure from data, also called . 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 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 data.
在本综述中，我们讨论了从数据中学习因果结构的方法，也称为 。特别地，我们关注学习有向无环图的方法以及各种推广，这些推广允许在可用数据中某些变量未被观测到。我们特别关注因果结构学习的两个基本组合方面。第一，我们讨论因果图搜索空间的结构。第二，我们讨论因果图上的 结构，即表示仅从观测数据中可以学到什么的图的集合，以及如何通过添加 数据来细化这些等价类。
