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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 6
1688-1692

Causation inference based on combining additive noise model and conditional independence

Mai Guizhen1a
Peng Shiguo1a
Hong Yinghan2
Chen Pinghua1b
Peng Yuzhong3
1. a. School of Automation, b. School of Computer Science & Technology, Guangdong University of Technology, Guangzhou 510006, China
2. School of Physics & Electronic Engineering, Hanshan Normal University, Chaozhou Guangdong 521041, China
3. Key Laboratory of Scientific Computing & Intelligent Information Processing, Guangxi Teachers Education University, Nanning 530001, China
DOI: 10.19734/j.issn.1001-3695.2017.12.0802

Abstract

Inferring causal directions from observed variables is one of the fundamental problems in artificial intelligence(AI) field. Traditional conditional independence based methods usually learn causal directions by detecting V-structures and return Markov equivalence classes, instead of true causal structures. Most other direction learning methods can distinguish the equivalence classes, but are effective only in the bivariate(or two-dimensional) cases. This paper proposd a new approach for causal direction inference from general networks, based on a split-and-merge strategy. The method first decomposed an n-dimensional network into n induced subnetworks, each of which corresponded to a node in the network. Each induced subnetwork could be subsumed to one of the three substructures: one-degree, non-triangle and triangle-existence structures. It deve-loped three effective algorithms to infer causalities from the three substructures, and learning these induced subnetworks orderly to achieved the whole causal structure of the multi-dimensional network. Experiments show that the method is more general and effective than traditional methods.

Foundation Support

国家自然科学基金资助项目(61374081,61562008)
广东省自然科学基金资助项目(S2013010013034)
广西自然科学基金资助项目(#GXNSFAA198228)
广东省科技项目(2014A030307049,2015A030401101,2015B090922014,2016B030306002,201604010099,2017A040405063,2016B030308001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.12.0802
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 6
Section: Algorithm Research & Explore
Pages: 1688-1692
Serial Number: 1001-3695(2019)06-019-1688-05

Publish History

[2019-06-05] Printed Article

Cite This Article

麦桂珍, 彭世国, 洪英汉, 等. 混合加噪声模型与条件独立性检测的因果方向推断算法 [J]. 计算机应用研究, 2019, 36 (6): 1688-1692. (Mai Guizhen, Peng Shiguo, Hong Yinghan, et al. Causation inference based on combining additive noise model and conditional independence [J]. Application Research of Computers, 2019, 36 (6): 1688-1692. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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