Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (1): 279-284.doi: 10.11896/jsjkx.210300028

• Artificial Intelligence • Previous Articles     Next Articles

Efficient Computation of Intervention in Causal Bayesian Networks

LI Chao1, QIN Biao2   

  1. 1 Business School,China University of Political Science and Law,Beijing 100088,China
    2 Information School,Renmin University of China,Beijing 100872,China
  • Received:2021-03-02 Revised:2021-06-14 Online:2022-01-15 Published:2022-01-18
  • About author:LI Chao,born in 1976,Ph.D,professor.Her main research interests include statistical machine learning and business intelligence.
    QIN Biao,born in 1972,Ph.D,vice professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include causal Baye-sian network and machine learning.
  • Supported by:
    Scientific Research and Innovation Project of China University of Political Science and Law(19ZFG79002),National Science Foundation of China(61772534),Research Foundation from Ministry of Education of China(19JHQ007),New Discipline Construction Project of China University of Political Science and Law and Fundamental Research Funds for the Central Universities.

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Abstract: In causal Bayesian networks (CBNs),it is a fundamental problem to compute the causal effect of sum product.From the perspective of a directed acyclic graph,we show every CBN has a corresponding Bayesian network.Intervention is a fundamental operation in CBNs.Similar to Bayesian networks,CBNs also have the pruning strategy.After pruning the barren nodes,this paper devises an optimized jointree algorithm to compute the full atomic intervention on each node in a CBN.Then,this paper explores the multiple interventions on multiple nodes,and finds that multiple interventions have the commutative property.On the basis of the commutative property in multiple interventions,this paper proves the strategies,which can be used to optimize the computation of the causal effect of multiple interventions.Finally,we report experimental results to demonstrate the efficiency of our algorithm to compute the causal effects in CBNs.

Key words: Barren nodes, Causal Bayesian networks, Full atomic intervention, Intervention, Multiple interventions

CLC Number: 

  • TP311
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[1] LI Chao, QIN Biao. Efficient Computation of MPE in Causal Bayesian Networks [J]. Computer Science, 2021, 48(4): 14-19.
[2] YUAN De-yu, CHEN Shi-cong, GAO Jian, WANG Xiao-juan. Intervention Algorithm for Distorted Information in Online Social Networks Based on Stackelberg Game [J]. Computer Science, 2021, 48(3): 313-319.
[3] YAO Ning, MIAO Duo-qian, ZHANG Zhi-fei. Transportability of Causal Information Across Different Granularities [J]. Computer Science, 2019, 46(2): 178-186.
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[5] HE Xiao-li,BI Gui-hong and WANG Hai-rui. Evaluating Heterosexual HIV Transmission and Interventions Based on Agent Bipartite Dynamic Weighting Scale-free Network [J]. Computer Science, 2014, 41(1): 72-79.
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