四川大学学报（自然科学版）2024，Vol.61Issue(2)：49-58,10.DOI:10.19907/j.0490-6756.2024.022002
基于因果反馈的缺失数据集因果关系发现
Causal feedback-based imputation causal discovery
摘要
Abstract
Causal discovery is an important part of causal inference and the goal is to discover the data genera-tion mechanism in the form of Directed Acyclic Graphs(DAGs).With regard to causal discovery,existing methods rarely take into account the presence of missing values in observational data.However,incomplete datasets are ubiquitous in practical scenarios,and figuring out the causal relationships in incomplete datasets has become a critical issue to be solved.In this paper,a new Causal Feedback-based Imputation Causal Dis-covery(CF-ICD)algorithm is proposed to achieve causal discovery of incomplete data sets.Generative Ad-versarial Networks(GAN)are used to estimate the distribution of missing data.The causal learning module based on Actor-Critic is used to search the optimal DAG,and a custom reward function based on the ex-tended Bayesian Information Criteria(eBIC)is designed.Classification error is introduced to guide the model to accelerate the exploration process and improve the stability.Extensive experimental results on simu-lated data and real data show that the proposed method is superior to existing methods under different data missing rates.关键词
深度学习/缺失数据补全/因果关系发现/有向无环图Key words
Deep learning/Data imputation/Causal discovery/Directed Acyclic Graph分类
信息技术与安全科学引用本文复制引用
马从锂,黄飞虎,弋沛玉,王琳娜,彭舰..基于因果反馈的缺失数据集因果关系发现[J].四川大学学报（自然科学版）,2024,61(2):49-58,10.基金项目
四川大学博士后交叉学科项目(10822041A2137) （10822041A2137）
四川省重点实验室开放课题(SCITLAB-20001) （SCITLAB-20001）
四川省科技厅项目(2023YFG0112,2022YFG0034) （2023YFG0112,2022YFG0034）
