基于Cai-伪残差与变量独立性的因果定向方法

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中图分类号：TP18 文献标志码：A DOI：10.13705/j.issn.1671-6841.2024102

文章编号：1671-6841（2025）06-0024-10

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 within the Markov equivalence class.Finally，experiments were performed on both linear Gaussian datasets and non-Gaussian datasets madeup 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.

Key Words：causal orientation；Bayesian network ； Markov equivalence class； pseudo residual; inde-pendence test

0 引言

在医学诊断、社会科学以及系统控制等多个领域，因果关系发现都有着广泛的应用[1-2]。（剩余10940字）