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Causal Discovery with Bayesian Networks Inductive Transfer

  • Haiyang Jia
  • Zuoxi Wu
  • Juan Chen
  • Bingguang Chen
  • Sicheng Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

Bayesian networks (BNs) is a dominate model for representing causal knowledge with uncertainty. Causal discovery with BNs requiring large amount of training data for learning BNs structure. When confronted with small sample scenario the learning task is a big challenge. Transfer learning motivated by the fact that people can intelligently apply knowledge learned previously to solve new problems faster or with better solutions, the paper defines a transferable conditional independence test formula which exploit the knowledge accumulated from data in auxiliary domains to facilitate learning task in the target domain, a BNs inductive transfer algorithm were proposed, which learning the Markov equivalence class of BNs. Empirical experiment was deployed, the results demonstrate the effectiveness of the inductive transfer.

Keywords

Bayesian network Inductive transfer Causal discovery Structure learning 

Notes

Acknowledgements

This paper is supported by National Natural Science Foundation of China under Grant Nos. 61502198, 61472161, 61402195, 61103091 and the Science and Technology Development Plan of Jilin Province under Grant No. 20160520099JH, 20150101051JC.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.College of SoftwareJilin UniversityChangchunChina
  3. 3.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunChina

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