Journal of Guangdong University of Technology ›› 2019, Vol. 36 ›› Issue (05): 14-19.doi: 10.12052/gdutxb.180146

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Learning Causal Skeleton by Using Lower Order Conditional Independent Tests

Hong Ying-han1,2, Hao Zhi-feng1,3, Mai Gui-zhen1, Chen Ping-hua1   

  1. 1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Physice & Electronic Engineering, Hanshan Normal University, Chaozhou 521041, China;
    3. School of Mathematics and Big Data, Foshan University, Foshan 528000, China
  • Received:2018-11-05 Online:2019-08-21 Published:2019-08-06

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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.

Key words: causal inference structure learning, high dimensional data, low order, conditional independent testing

CLC Number: 

  • TP301.6
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