Work

A novel constraint-based structure learning algorithm using marginal causal prior knowledge

Year: 2024

Type: article

Abstract: 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.

Source: Scientific Reports

DOI: 10.1038/s41598-024-68379-7

Authors: Yifan Yu, Lei Hou, Xinhui Liu, Sijia Wu, Hongkai Li, Fuzhong Xue

Institution: Shandong University

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,590

Funder: National Key Research and Development Program of China