计算机工程2024，Vol.50Issue(3)：131-136,6.DOI:10.19678/j.issn.1000-3428.0066564
基于因果自回归流模型的因果结构学习算法
Causal Structure Learning Algorithm Based on Causal Autoregressive Flow Model
摘要
Abstract
The causal autoregressive flow model has realized promising results on the causal direction inference problem when the noise is affected by parent nodes.However,to date,existing methods suffer from low accuracy and high computational cost due to the global structure search.Therefore,in this study,a two-stage causal structure learning algorithm is designed for non-temporal observation data.The first stage involves obtaining the basic causal skeleton based on the conditional independence of the observed data from a completely undirected graph,and the second stage involves inferring causal direction by using normalizing flow to compare the edge likelihood probability in different directions based on the causal autoregressive flow model.The experiments on the simulated data shows that the proposed algorithm outperforms the existing mainstream causal structure learning algorithm,and the F1 score of the proposed algorithm is 15%-28%higher than the baseline methods.Similarly on the real world data,when compared with the mainstream causal learning algorithms,the proposed algorithm can learn the causal relationship more completely and accurately,and the F1 score of the proposed algorithm is 28%-48%higher than the baseline methods.Experimental results demonstrate the stronger robustness of the proposed algorithm.关键词
因果结构学习/因果发现/加性噪声模型/因果自回归流模型/标准化流Key words
causal structure learning/causal discovery/additive noise model/causal autoregressive flow model/normalizing flow分类
信息技术与安全科学引用本文复制引用
卢小金,陈薇,郝志峰,蔡瑞初..基于因果自回归流模型的因果结构学习算法[J].计算机工程,2024,50(3):131-136,6.基金项目
国家自然科学基金(61876043,61976052,62206064) （61876043,61976052,62206064）
科技创新2030-"新一代人工智能"重大项目(2021ZD0111501) （2021ZD0111501）
国家优秀青年科学基金(62122022). （62122022）
