There are several existing algorithms that under appropriate assumptions can
reliably identify a subset of the underlying causal relationships from
observational data. This paper introduces the first computationally feasible
score-based algorithm that can reliably identify causal relationships in the
large sample limit for discrete models, while allowing for the possibility that
there are unobserved common causes. In doing so, the algorithm does not ever
need to assign scores to causal structures with unobserved common causes. The
algorithm is based on the identification of so called Y substructures within
Bayesian network structures that can be learned from observational data. An
example of a Y substructure is A -> C, B -> C, C -> D. After providing
background on causal discovery, the paper proves the conditions under which the
algorithm is reliable in the large sample limit.

本文介绍了第一种可进行大样本因果关系可靠识别的计算可行的基于分数的算法，允许存在未观测到的共同原因，并基于从观测数据中学习的贝叶斯网络结构来识别所谓的 Y 子结构，从而无需为存在未观察到的共同原因的因果结构分配分数。