论文标题
在马尔可夫等效类别中，所有可能的总效果的最小枚举
Minimal enumeration of all possible total effects in a Markov equivalence class
论文作者
论文摘要
在观察性研究中，当未确定关注的全部因果效应时，可以报告所有可能的影响的集合。通常，当基本的因果DAG仅知道到马尔可夫等效类别或由于背景知识而进行改进时，这通常会发生。因此，可能的因果关系类别由最大取向的部分定向的无环图（MPDAG）表示，该图均包含有向和无方向的边缘。我们表征了确定给定的总效果所需的最小额外边缘方向。然后开发出递归算法以枚举DAG的亚类，以便将每个亚类的总效应鉴定为观察到的分布的独特功能。这解决了现有方法的问题，现有方法通常会报告重复的总效应，即由于抽样变异性而在数值上截然不同的方法，但实际上是因果关系相同的。
In observational studies, when a total causal effect of interest is not identified, the set of all possible effects can be reported instead. This typically occurs when the underlying causal DAG is only known up to a Markov equivalence class, or a refinement thereof due to background knowledge. As such, the class of possible causal DAGs is represented by a maximally oriented partially directed acyclic graph (MPDAG), which contains both directed and undirected edges. We characterize the minimal additional edge orientations required to identify a given total effect. A recursive algorithm is then developed to enumerate subclasses of DAGs, such that the total effect in each subclass is identified as a distinct functional of the observed distribution. This resolves an issue with existing methods, which often report possible total effects with duplicates, namely those that are numerically distinct due to sampling variability but are in fact causally identical.
