《计算机应用研究》|Application Research of Computers

混合加噪声模型与条件独立性检测的因果方向推断算法

Causation inference based on combining additive noise model and conditional independence

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作者 麦桂珍,彭世国,洪英汉,陈平华,彭昱忠
机构 1.广东工业大学 a.自动化学院;b.计算机学院,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.广西师范学院 科学计算与智能信息处理广西高校重点实验室,南宁 530001
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文章编号 1001-3695(2019)06-019-1688-05
DOI 10.19734/j.issn.1001-3695.2017.12.0802
摘要 从可观测的变量中推导出潜在的因果关系是人工智能领域的热点研究之一。传统的基于独立性检测的方法是通过检测V结构来确定一组马尔可夫等价类而非最终的因果关系;而加噪声模型算法却只能适应于低维度的因果网络结构。为此,提出一种采取分治策略的混合加噪声模型与条件独立性检测的因果方向推断方法。首先将一个<i>n</i>维因果网络分解成<i>n</i>个诱导子网络,分别归入三种基本结构(单度结构、非三角结构和存在三角的结构)中的一种,从理论上分别证明其有效性;其次对每个诱导子网络进行基于加噪声模型算法与条件独立性检测相结合的方向推断;最后把所有子网络合并起来构建成完整的因果关系网络。实验表明,该方法比传统的因果关系推断方法更加有效。
关键词 因果网络; 加噪声模型; 马尔可夫等价类
基金项目 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)
本文URL http://www.arocmag.com/article/01-2019-06-019.html
英文标题 Causation inference based on combining additive noise model and conditional independence
作者英文名 Mai Guizhen, Peng Shiguo, Hong Yinghan, Chen Pinghua, Peng Yuzhong
机构英文名 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
英文摘要 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 <i>n</i>-dimensional network into <i>n</i> 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.
英文关键词 causal networks; additive noise model; Markov equivalence classes
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收稿日期 2017/12/10
修回日期 2018/1/30
页码 1688-1692
中图分类号 TP301.6
文献标志码 A