Yu Yifan, Hou Lei, Liu Xinhui, Wu Sijia, Li Hongkai, Xue Fuzhong
Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhua West Road, Jinan, Shandong Province, 250000, People's Republic of China.
Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China, 250000.
Sci Rep. 2024 Aug 20;14(1):19279. doi: 10.1038/s41598-024-68379-7.
Causal discovery with prior knowledge is important for improving performance. We consider the incorporation of marginal causal relations, which correspond to the presence or absence of directed paths in a causal model. We propose the Marginal Prior Causal Knowledge PC (MPPC) algorithm to incorporate marginal causal relations into a constraint-based structure learning algorithm. We provide the theorems of conditional independence properties by combining observational data and marginal causal relations. We compare the MPPC algorithm with other structure learning methods in both simulation studies and real-world networks. The results indicate that, compare with other constraint-based structure learning methods, MPPC algorithm can incorporate marginal causal relations and is more effective and more efficient.
利用先验知识进行因果发现对于提高性能很重要。我们考虑纳入边际因果关系，其对应于因果模型中是否存在有向路径。我们提出了边际先验因果知识PC（MPPC）算法，将边际因果关系纳入基于约束的结构学习算法。通过结合观测数据和边际因果关系，我们给出了条件独立性属性的定理。在模拟研究和现实世界网络中，我们将MPPC算法与其他结构学习方法进行了比较。结果表明，与其他基于约束的结构学习方法相比，MPPC算法能够纳入边际因果关系，并且更有效、更高效。
