arXiv cs.AI· Jiangming Zhong, Jiawei Zhu, Guanting Wu, Qiaoxi Cai, Wanyi Chen, Jiale Li·· 1 天前AI 评分22
CausalGrade：基于 Jev 的证据引导因果图评分方法
CausalGrade: Evidence-Guided Causal Graph Scoring with Jev
AI 导读
CausalGrade 提出一种基于概率判断模型 Jev 的因果图评分方法，联合利用观测数据的关联、条件依赖与方向性证据以及因果发现基础模型 CDFM 输出的边概率，通过图邻接结构与边级判断的一致性得到图级分数。
正文 · 原文
Abstract:Evaluating the structural quality of candidate causal graphs when the true causal structure is unknown is an important problem in applying causal discovery results. This paper proposes CausalGrade, a causal graph scoring method based on the probabilistic judgment model Jev. The method jointly provides Jev with evidence of association, conditional dependence, and directionality from observational data and edge probabilities produced by the causal discovery foundation model CDFM. Jev produces probabilistic judgments about direct causal edges, and a graph-level score is obtained by measuring the agreement between the adjacency structure of the graph under evaluation and these edge-level judgments. Across 300 synthetic datasets, the score exhibits a relatively strong negative correlation with structural error in all 20 combinations of four causal discovery algorithms and five variable counts, indicating that higher scores generally correspond to smaller structural errors. In 500 synthetic graph selection tasks, CausalGrade achieves a best-candidate hit rate of 54.4\%, exceeding the 48.4\% achieved by the Self-Compatibility metric \(\kappa_G\), further supporting its ability to discriminate the structural quality of graphs under evaluation.
| Comments: | 6 pages, 2 figures |
| Subjects: | Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.40692 [stat.ML] |
| (or arXiv:2610.40692v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40692 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiangming Zhong [view email]
[v1]
Wed, 28 Oct 2026 07:33:20 UTC (894 KB)
来源：arXiv cs.AI · arxiv.org