Learning Causal Skeleton by Using Lower Order Conditional Independent Tests

Hong Ying-han; Hao Zhi-feng; Mai Gui-zhen; Chen Ping-hua

doi:10.12052/gdutxb.180146

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Learning Causal Skeleton by Using Lower Order Conditional Independent Tests

更新时间：2026-05-27

- Learning Causal Skeleton by Using Lower Order Conditional Independent Tests
- Vol. 36, Issue 5, Pages: 14-19(2019)
- 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：
- 基金信息：
- DOI：10.12052/gdutxb.180146
  CLC： TP301.6
- Online First：21 August 2019，
  
  Published：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

CAI R C, ZHANG Z J, HAO Z F. Sada: a general framework to support robust causation discovery[C]//International Conference on Machine Learning. Atlanta, GA, USA: ICML, 2013, 28: 208-216.

XIE X C, GENG Z. A recursive method for structural learning of directed acyclic graphs [J]. Journal of Machine Learning Research, 2008, 9(3): 459-483.

GENG Z, WANG C, ZHAO Q. Decomposition of search for v-structures in dags [J]. Journal of Multivariate Analysis, 2005, 96(2): 282-294.

XIE X C, GENG Z, ZHAO Q. Decomposition of structural learning about directed acyclic graphs [J]. Artificial Intelligence, 2006, 170(4-5): 422-439.

LIU H, ZHOU S G, LAM W, et al . A new hybrid method for learning bayesian networks: separation and reunion [J ] . Knowledge-Based Systems, 2017, 121: 185-197.

PEARL J. Causality[M]. Cambridge: Cambridge University Press, 2009: 639-648.

KLOKS T, KRATSCH D. Finding all minimal separators of a graph [J]. Lecture Notes in Computer Science, 1993, 842(3): 759-768.

ZHANG K, PETERS J, JANZING D, et al . Kernel-based conditional independence test and application in causal discovery [J ] . Computer Science, 2012, 06(8): 895-907.

EDWARDS D. Introduction to graphical modelling[M]. New York: Springer, 2000: 235-254.

FUKUMIZU K, GRETTON A, SUN X, et al . Kernel measures of conditional dependence [J ] . Advances in Neural Information Processing Systems, 2007, 20(1): 167-204.

ZHANG H, ZHOU S G, ZHANG K, et al. Causal discovery using regression-based conditional independence tests[C]// AAAI Conference on Artificial Intelligence. San Francisco, California, USA: AAAI, 2017: 1250-1256.

ZHANG K, PETERS J, JANZING D, et al. Kernel-based conditional independence test and application in causal discovery[M]. USA: AUAI Press, 2011: 804-813.

ZHANG K, WANG Z K, ZHANG J J, et al. On estimation of functional causal models: general results and application to post-nonlinear causal model[C]// ACM Transactions on Intelligent Systems and Technologies. New York, NY, USA: ACM, 2015, 7(2): 1-22.

CAI R C, ZHANG Z J, HAO Z F. Bassum: a bayesian semi-supervised method for classification feature selection [J]. Pattern Recognition, 2011, 44(4): 811-820.

CAI R C, ZHANG Z J, HAO Z F. Causal gene identification using combinatorial v-structure search [J]. Neural Networks, 2013, 43: 63-71.

MOOIJ J M, PETERS J, JANZING D, et al . Distinguishing cause from effect using observational data: methods and benchmarks [J ] . The Journal of Machine Learning Research, 2016, 17(1): 1103-1204.

HOYER P O, JANZING D, MOOIJ J M, et al . Nonlinear causal discovery with additive noise models [J ] . Advances in neural Information Processing Systems, 2009: 689-696.

PETERS J, JANZING D, SCHOLKOPF B. Causal inference on discrete data using additive noise models[C]//IEEE Transactions on Pattern Analysis Machine Intelligence. Washington DC: IEEE Computer Society, 2011: 2436-2450.

SHIMIZU S, HOYER P. O, HYVARINEN A, et al A linear non-Gaussian acyclic model for causal discovery [J ] . the Journal of Machine Learning Research, 2006, 7: 2003-2030.

JUDEA P. Probability reasoning in intelligent systems networks of plausible inference [J]. Computer Science Artificial Intelligence, 1988, 70(2): 1022-1027.

KOLLER D, FRIEDMAN N. Probabilistic graphical models: principles and techniques[M]. Cambridge: Massachusetts Institute of Technology Press, 2009: 56-72.

BUDHATHOKI K, VREEKEN J. Causal inference by compression[C]// 2016 IEEE 16th International Conference on Data Mining. Barcelona, Spain: IEEE, 2016: 41-50.

JANZING D, STEUDEL B, SHAJARISALES N, et al . Justifying information-geometric causal inference [J ] . Measures of Complexity, 2015: 253-265.

SCHOLKOPF B, JANZING D, PETERS J, et al. Semi-supervised learning in causal and anticausal settings[M]. Berlin Heidelberg: Springer, 2013: 129-141.

SGOURITSA E, JANZING D, HENNIG P, et al . Inference of cause and effect with unsupervised inverse regression [J ] . Artificial Intelligence and Statistics, 2015: 847-855.

SPIRTES P, GLYMOUR C N, SCHEINES R. Causation, prediction and search[M]. New York: Springer, 1993, 45(3): 272-273.

BABA K, SHIBATA R, SIBUYA M. Partial correlation and conditional correlation as measures of conditional independence [J]. Australian and New Zealand Journal of Statistics, 2004, 46(4): 657-664.

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