基于低阶条件独立测试的因果网络结构学习方法

洪英汉; 郝志峰; 麦桂珍; 陈平华

doi:10.12052/gdutxb.180146

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基于低阶条件独立测试的因果网络结构学习方法

综合研究 | 更新时间：2026-05-27

- 基于低阶条件独立测试的因果网络结构学习方法
- Learning Causal Skeleton by Using Lower Order Conditional Independent Tests
- 广东工业大学学报 2019年36卷第5期页码：14-19
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 韩山师范学院物理与电子工程学院,广东,潮州,521041
  3. 佛山科学技术学院数学与大数据学院,广东,佛山,528000
- 作者简介：
  
  洪英汉(1984–)，男，副教授，博士研究生，主要研究方向为因果关系、机器学习、云计算、数据挖掘及其应用
  麦桂珍(1985–)，女，博士，主要研究方向为因果关系、机器学习. E-mail：mgz0323@126.com
- 基金信息：
  
  国家自然科学基金资助项目(61472089，61572144);广东省科技计划项目(2015A030101101，2015B090922014，2017A040405063，2017B030307002)
- DOI：10.12052/gdutxb.180146
  中图分类号： TP301.6
- 网络首发：2019-08-21，
  
  纸质出版：2019
- 稿件说明：
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洪英汉, 郝志峰, 麦桂珍, 陈平华. 基于低阶条件独立测试的因果网络结构学习方法[J]. 广东工业大学学报, 2019, 36(5): 14-19.

Hong Ying-han, Hao Zhi-feng, Mai Gui-zhen, et al. Learning Causal Skeleton by Using Lower Order Conditional Independent Tests[J]. 2019, 36(5): 14-19.
洪英汉, 郝志峰, 麦桂珍, 陈平华. 基于低阶条件独立测试的因果网络结构学习方法[J]. 广东工业大学学报, 2019, 36(5): 14-19. DOI： 10.12052/gdutxb.180146.

Hong Ying-han, Hao Zhi-feng, Mai Gui-zhen, et al. Learning Causal Skeleton by Using Lower Order Conditional Independent Tests[J]. 2019, 36(5): 14-19. DOI： 10.12052/gdutxb.180146.

摘要

基于条件约束的方法可从数据集中学习到变量间的因果关系

并构建出因果网络图. 但是在高维数据情况下

基于条件约束方法的缺点是准确率较低且耗时多

从而严重影响此类方法在高维数据中的应用推广. 因此

本文提出了一种基于低阶条件独立测试的因果网络结构学习方法

采用低阶条件独立测试来加速构建因果粗糙骨架；利用分裂−合并策略把高维网络分裂成若干个子网络

并进行因果网络结构学习以提高其准确率；最后整合成完整的因果网络图. 实验结果均验证了该方法的可行性.

Abstract

The causal relationships between variables from dataset and the corresponding causal network can be recovered by the constraint-based methods. However

in high-dimensional dataset

the accuracy and efficiency of the constraint-based methods are not high

which seriously affects the application and promotion of such methods in high-dimensional dataset. In order to solve these problems

a causal network structure learning method based on low-order conditional independent (CI) test is proposed. In this method

CI test based on lower order conditional set is used to construct a rough causality skeleton; and the split-merge strategy is taken to divide the large rough causality skeleton into a set of smaller subnetworks. The structure of each subnetwork of lower dimensionality is constructed independently

thus able to improve its accuracy. Finally

a complete causality network graph is integrated. The experimental results demonstrate the technical feasibility.

关键词

Keywords

references

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