计算机技术与发展2025，Vol.35Issue(3)：117-124,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0342
马尔可夫毯与多数投票因果发现评估
Markov Blanket and Majority Voting for Causal Discovery Assessment
摘要
Abstract
In recent years,causal learning has successfully merged with deep learning due to its excellent interpretability.In causal learning,the collection of natural data is often difficult and costly,leading past research to primarily rely on synthetic datasets for validating causal discovery.However,synthetic and semi-real datasets often involve significant artificial control and fail to accurately reflect the performance of causal discovery algorithms in real-world scenarios.To address this issue,we propose a novel strategy for e-valuating causal discovery methods in the absence of true causal graphs.Specifically,we divide the dataset into training and testing sets,perform causal discovery on the training set to construct a causal graph,and then validate this causal graph using the testing set.The vali-dation process includes Markov blanket tests and causal direction identification for each edge in the causal graph,with the final results in-tegrated using a majority voting strategy.We conducted extensive experiments on both synthetic and real datasets,and the results demonstrate that the proposed method effectively evaluates the accuracy and generalizability of causal graphs.The proposed method provides a new approach for assessing the performance of causal discovery algorithms in real-world settings,enhancing the applicability and reliability of causal learning.关键词
因果发现/马尔可夫毯测试/数据集分割/多数投票策略/因果非对称识别方法Key words
causal discovery/Markov blanket testing/data split/majority vote strategy/asymmetric causality identification method分类
计算机与自动化引用本文复制引用
李廷鹏,王雷,彭丹华,廖军,刘礼..马尔可夫毯与多数投票因果发现评估[J].计算机技术与发展,2025,35(3):117-124,8.基金项目
电子信息系统复杂电磁环境效应国家重点实验室项目(CEMEE2023G0202) （CEMEE2023G0202）
国家自然科学基金(62207007) （62207007）
国家重大研发计划(2022YFB3303302) （2022YFB3303302）
