计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 198-203.doi: 10.11896/jsjkx.210100053
乔杰1, 蔡瑞初1, 郝志峰2
QIAO Jie1, CAI Rui-chu1, HAO Zhi-feng2
摘要: 观测数据因果关系挖掘是很多学科的基础问题。然而基于约束与因果函数等的现有方法对数据的因果机制具有较强的假设,一般适用于低维数据,并不能很好地适用于存在隐变量的场景。为此,提出了一种基于信息瓶颈的因果关系挖掘方法,称为因果信息瓶颈方法。该方法将因果机制划分为压缩与提取两阶段,在压缩阶段,假设存在一个经过压缩的中间隐变量,在提取阶段,可能保留与结果变量相关的信息。在上述建模的基础上,通过推导其变分上界,设计了一种的基于变分自编码机的因果关系挖掘方法。实验结果表明,基于信息瓶颈的方法在合成数据中准确率提升了10%,在真实数据中准确率提升了4%。
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[1]MCINERNEY J,BROST B,CHANDAR P,et al.Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions[C]//The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.New York:ACM,2020:1779-1788. [2]RUNGE J,BATHIANY S,BOLLT E,et al.Inferring causationfrom time series in Earth system sciences[J].Nature Communications,2019,10(1):1-13. [3]CAI R,ZHANG Z,HAO Z,et al.Understanding social causali-ties behind human action sequences[J].IEEE Transactions on Neural Networks and Learning Systems,2016,28(8):1801-1813. [4]WANG W J,DU X H,REN Z Y,et al.Reconstruction of Cloud Platform Attack Scenario Based on Causal Knowledge and Temporal-Spatial Correlation[J].Computer Science,48(2):317-323. [5]CAI R C,CHEN W,ZHANG K,et al.A Survey on Non-Temporal Series Observational Data based Causal Discovery[J].Chinese Journal of Computers,2017,40(6):1470-1490. [6]XIE F,CAI R,HUANG B,et al.Generalized Independent NoiseCondition for Estimating Latent Variable Causal Graphs[C]//Advances in Neural Information Processing Systems.New York:Curran Associates,Inc.,2020:14891-14902. [7]GLYMOUR C,ZHANG K,SPIRTES P.Review of causal discovery methods based on graphical models[J].Frontiers in Genetics,2019,10:524. [8]SHIMIZU S,HOYER P O,HYVÄRINEN A,et al.A linear non-Gaussian acyclic model for causal discovery[J].Journal of Machine Learning Research,2006,7(Oct):2003-2030. [9]SHIMIZU S,INAZUMI T,SOGAWA Y,et al.DirectLiNGAM:A direct method for learning a linear non-Gaussian structural equation model[J].Journal of Machine Learning Research,2011,12(Apr):1225-1248. [10]HOYER P,JANZING D,MOOIJ J M,et al.Nonlinear causal discovery with additive noise models[C]//Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems.New York:NIPS,2008:689-696. [11]ZHANG K,HYVÄRINEN A.On the Identifiability of the Post-Nonlinear Causal Model[C]//UAI 2009,Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence.Corvallis,USA:AUAI Press,2009:647-655. [12]CAI R,QIAO J,ZHANG K,et al.Causal discovery from discrete data using hidden compact representation[C]//Advances in Neural Information Processing Systems.Calfornia,USA:NIPS,2018:2666-2674. [13]CAI R,QIAO J,ZHANG K,et al.Causal discovery with cascade nonlinear additive noise models[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.Palo Alto,CA:AAAI Press,2019:1609-1615. [14]HUANG YL,LI P F,ZHU Q M.Joint Model of Events' Causal and Temporal Relations Identification[J].Computer Science,2018,45(6):204-207,234. [15]SPIRTES P,GLYMOUR C N,SCHEINES R.Causation,prediction,and search[M].USA:MIT press,2000. [16]TSAMARDINOS I,BROWN L E,ALIFERIS C F.The max-min hill-climbing Bayesian network structure learning algorithm[J].Machine Learning,2006,65(1):31-78. [17]ANDERSSON S A,MADIGAN D,PERLMAN M D,et al.A characterization of Markov equivalence classes for acyclic digraphs[J].The Annals of Statistics,Institute of Mathematical Statistics,1997,25(2):505-541. [18]SLONIM N,FRIEDMAN N,TISHBY N.Multivariate information bottleneck[J].Neural Computation,2006,18(8):1739-1789. [19]ALEMI A A,FISCHER I,DILLON J V,et al.Deep Variational Information Bottleneck[C]//the 5th International Conference on Learning Representations.2017. [20]KINGMA D P,WELLING M.Auto-Encoding Variational Bayes[C]//the 2nd International Conference on Learning Representations.2014. [21]HORNIK K,STINCHCOMBE M B,WHITE H.Multilayerfeedforward networks are universal approximators[J].Neural Networks,1989,2(5):359-366. [22]KINGMA D P,BA J.Adam:A Method for Stochastic Optimization[C]//the 3rd International Conference on Learning Representations.2014. [23]BÜHLMANN P,PETERS J,ERNEST J,et al.CAM:Causaladditive models,high-dimensional order search and penalized regression[J].The Annals of Statistics,Institute of Mathematical Statistics,2014,42(6):2526-2556. [24]GRETTON A,BOUSQUET O,SMOLA A J,et al.Measuring Statistical Dependence with Hilbert-Schmidt Norms[C]//Algorithmic Learning Theory,16th International Conference.Berlin,German:Springer,2005:63-77. [25]MOOIJ J M,PETERS J,JANZING D,et al.Distinguishingcause from effect using observational data:methods and benchmarks[J].The Journal of Machine Learning Research,2016,17(1):1103-1204. |
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