[1] PEARL J, MACKENZIE D.The book of why:the new science of cause and effect[M].New York, USA:ACM Press, 2018. [2] 郑巧夺, 吴贞东, 邹俊颖.基于双层CNN-BiGRU-CRF的事件因果关系抽取[J].计算机工程, 2021, 47(5):58-64, 72. ZHENG Q D, WU Z D, ZOU J Y.Event causality extraction based on two-layer CNN-BiGRU-CRF[J].Computer Engineering, 2021, 47(5):58-64, 72.(in Chinese) [3] 姚宏亮, 马晓琴, 王浩, 等.基于形态特征与因果岭回归的股市态势预测算法[J].计算机工程, 2016, 42(2):175-183. YAO H L, MA X Q, WANG H, et al.Stock market trend prediction algorithm based on morphological characteristics and causal ridge regression[J].Computer Engineering, 2016, 42(2):175-183.(in Chinese) [4] HUANG B W, ZHANG K.Specific and shared causal relation modeling and mechanism-based clustering[C]//Proceedings of NeurIPS 2019.Vancouver, Canada:[s.n.], 2019:13510-13521. [5] SACHS K, PEREZ O, PE'ER D, et al.Causal protein-signaling networks derived from multiparameter single-cell data[J].Science, 2005, 308(5721):523-529. [6] CHEN W, CAI R C, HAO Z F, et al.Mining hidden non-redundant causal relationships in online social networks[J].Neural Computing and Applications, 2020, 32(11):6913-6923. [7] ENTNER D, HOYER P O.On causal discovery from time series data using FCI[EB/OL].[2021-10-12].https://www.researchgate.net/publication/268324455. [8] RUNGE J, NOWACK P, KRETSCHMER M, et al.Detecting and quantifying causal associations in large nonlinear time series datasets[J].Science Advances, 2019, 5(11):1-10. [9] PAMFIL R, SRIWATTANAWORACHAI N, DESAI S, et al.DYNOTEARS:structure learning from time-series data[C]//Proceedings of International Conference on Artificial Intelligence and Statistics.[S.l.]:PMLR, 2020:1595-1605. [10] HYVÄRINEN A, ZHANG K, SHIMIZU S, et al.Estimation of a structural vector autoregression model using non-Gaussianity[J].Journal of Machine Learning Research, 2010, 11(5):1-10. [11] 蔡瑞初, 陈薇, 张坤, 等.基于非时序观察数据的因果关系发现综述[J].计算机学报, 2017, 40(6):1470-1490. 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.(in Chinese) [12] SPIRTES P, GLYMOUR C.An algorithm for fast recovery of sparse causal graphs[J].Social Science Computer Review, 1991, 9(1):62-72. [13] CHICKERING D M.Optimal structure identification with greedy search[J].Journal of Machine Learning Research, 2002, 3(Nov):507-554. [14] 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(10):1-10. [15] HOYER P O, JANZING D, MOOIJ J M, et al.Nonlinear causal discovery with additive noise models[C]//Proceedings of the 22nd Annual Conference on Neural Information Processing Systems.Vancouver, Canada:[s.n.], 2008:689-696. [16] ZHANG K, HYVARINEN A.On the identifiability of the post-nonlinear causal model[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence.New York, USA:ACM Press, 2018:647-655. [17] COLOMBO D, MAATHUIS M H.Order-independent constraint-based causal structure learning[J].Journal of Machine Learning Research, 2014, 15(1):3741-3782. [18] HU S B, CHEN Z T, NIA V P, et al.Causal inference and mechanism clustering of a mixture of additive noise models[EB/OL].[2021-10-12].https://arxiv.org/abs/1809.08568. [19] MACQUEEN J.Some methods for classification and analysis of multivariate observations[C]//Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, USA:University of California Press, 1967, 1(14):281-297. [20] KINGMA D P, WELLING M.Auto-encoding variational Bayes[EB/OL].[2021-10-12].https://arxiv.org/abs/1312. 114. [21] ERDOS P, RÉNYI A.On the evolution of random graphs[J].Publications of the Mathematical Institute of the Hungarian Acdemy of Sciences, 1960, 5(1):17-60. [22] SAKOE H, CHIBA S.Dynamic programming algorithm optimization for spoken word recognition[J].IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978, 26(1):43-49. [23] ESTER M, KRIEGEL H P, SANDER J, et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining.New York, USA:ACM Press, 1996:226-231. [24] ANKERST M, BREUNIG M M, KRIEGEL H P, et al.OPTICS:ordering points to identify the clustering structure[J].ACM Sigmod Record, 1999, 28(2):49-60. [25] RAND W M.Objective criteria for the evaluation of clustering methods[J].Journal of the American Statistical Association, 1971, 66(336):846-850. |