郑州大学学报（理学版）2025，Vol.57Issue(6)：24-33,10.DOI:10.13705/j.issn.1671-6841.2024102
基于Cai-伪残差与变量独立性的因果定向方法
Causal Orientation Method Based on the Independence of Cai-pseudo Residuals and Variables
摘要
Abstract
In addressing the issues of Markov equivalence class in constraint-based causal discovery methods and the non-Gaussian noise assumption in functional causal models,the Cai-pseudo residual causal orientation algorithm was proposed using the three theorems of the Cai-pseudo residuals.Firstly,the relationships between variables were assumed to be linear,and no restrictions were imposed on the type of noise.With these conditions,the independence between the Cai-pseudo residuals and variables was manifested in diverse ways across the three distinct structures of Bayesian networks.Secondly,after construction of the Markov equivalence class using a constraint-based method,such varying associations were exploited to further distinguish the three structures and direct some previously undirected edges with-in the Markov equivalence class.Finally,experiments were performed on both linear Gaussian datasets and non-Gaussian datasets made up of different causal network structures.The results highlighted that the proposed algorithm not only greatly lessened the quantity of undirected edges in the Markov equivalence class,but also notably enhanced the accuracy of causal direction determination.关键词
因果定向/贝叶斯网络/马尔科夫等价类/伪残差/独立性检验Key words
causal orientation/Bayesian network/Markov equivalence class/pseudo residual/inde-pendence test分类
信息技术与安全科学引用本文复制引用
牛瑞琪,原泽鹏,翟岩慧,赵延新,李德玉..基于Cai-伪残差与变量独立性的因果定向方法[J].郑州大学学报（理学版）,2025,57(6):24-33,10.基金项目
国家自然科学基金项目(62072294,61972238) （62072294,61972238）
