Many state-of-the-art causal discovery methods aim to generate an output graph that encodes the graphical separation and connection statements of the causal graph that underlies the data-generating process. In this work, we argue that an evaluation of a causal discovery method against synthetic data should include an analysis of how well this explicit goal is achieved by measuring how closely the separations/connections of the method's output align with those of the ground truth. We show that established evaluation measures do not accurately capture the difference in separations/connections of two causal graphs, and we introduce three new measures of distance called s/c-distance, Markov distance and Faithfulness distance that address this shortcoming. We complement our theoretical analysis with toy examples, empirical experiments and pseudocode.

翻译：许多前沿的因果发现方法旨在生成一个输出图，该图编码了生成数据过程的因果图所蕴含的图分离与连接关系。本文认为，针对合成数据评估因果发现方法时，应通过衡量方法输出的分离/连接关系与真实情况的吻合程度，来分析该明确目标的达成效果。我们论证了现有评估指标无法准确刻画两个因果图在分离/连接关系上的差异，并提出了三种新的距离度量：s/c距离、马尔可夫距离和忠实性距离，以弥补这一缺陷。我们通过简易实例、实证实验及伪代码对理论分析进行了补充。