计算机工程2023，Vol.49Issue(12)：63-70,8.DOI:10.19678/j.issn.1000-3428.0066474
基于结构方程似然框架的缺失值因果学习算法
Missing Value Causal Learning Algorithm Based on Structural Equation Likelihood Framework
摘要
Abstract
Exploring causal relationships between entities is crucial in data science.In practical scenarios missing values pose significant challenges to both constraint-based and structural equation model-based methods.Although existing causal learning methods effectively address random missing data,discerning causal structures in non-random missing data remains problematic.Challenges include learning causal pairs,identifying Markov equivalence class structures,and correcting causal direction errors in causal structure networks.To tackle these issues,this paper introduces a novel algorithm,MV-SELF,based on the structural equation likelihood framework.This algorithm transforms the conditional probability distribution of a nonlinear Additive Noise Model(ANM)into a representation of noise distribution.Consequently,it enables a maximum likelihood-based scoring mechanism for causal structure search.Additionally,MV-SELF utilizes Inverse Probability Weight(IPW)correction to counteract non-random deletions.This approach effectively restores the joint distribution of missing data,thereby correcting redundant edges and inaccurate causal directions.It facilitates high-dimensional causal structure searches in datasets with missing values.Simulation experiments reveal that MV-SELF outperforms TD-PC,MVPC,and Structure EM algorithms,achieving a 3%to 19%increase in F1 value.This improvement highlights MV-SELF's effectiveness in distinguishing Markov equivalence classes.关键词
结构方程似然框架/缺失数据/逆概率加权/因果方向学习/加性噪声模型Key words
structural equation likelihood framework/missing datas/Inverse Probability Weight(IPW)/causal discovery learning/Additive Noise Model(ANM)分类
信息技术与安全科学引用本文复制引用
郝志峰,喻建华,乔杰,蔡瑞初..基于结构方程似然框架的缺失值因果学习算法[J].计算机工程,2023,49(12):63-70,8.基金项目
国家自然科学基金(61876043,61976052,62206064) （61876043,61976052,62206064）
国家优秀青年科学基金(62122022) （62122022）
科技创新2030—"新一代人工智能"重大项目(2021ZD0111501). （2021ZD0111501）
