[1] MATTMANN C A. A vision for data science[J]. Nature, 2013, 493(7433):473-475. [2] PEARL J. Causality:Models, Reasoning, and Inference[M]. 2nd ed. New York:Cambridge University Press, 2009:1-55. [3] COOPER G F, YOO C. Causal discovery from a mixture of experimental and observational data[C]//Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence. San Francisco:Morgan Kaufmann Publishers Inc., 1999:116-125. [4] 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.) [5] SPIRTES P, MEEK C, RICHARDSON T. An algorithm for causal inference in the presence of latent variables and selection bias[M]//COOPER G F, GLYMOUR C. Computation Causation, and Discovery. Menlo Park, CA:AAAI Press, 1999:211-252. [6] CHAVES R, MAJENZ C, GROSS D. Information-theoretic implications of quantum causal structures[J]. Nature Communications, 2014, 6:No. 5766. [7] CAI R C, QIAO J, ZHANG K, et al. Causal discovery from discrete data using hidden compact representation[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook, NY:Curran Associates Inc., 2018:2671-2679. [8] SPIRTES P, GLYMOUR C, SCHEINES R. Causation, Prediction, and Search[M]. 2nd ed. Cambridge:MIT Press, 2001:35-40. [9] VERMA T, PEARL J. Equivalence and synthesis of causal models[C]//Proceedings of the 6th Annual Conference on Uncertainty in Artificial Intelligence. New York:Elsevier Science Inc., 1990:255-270. [10] SHIMIZU S, HOYER P O, HYVÄRINEN A, et al. A linear nonGaussian acyclic model for causal discovery[J]. Journal of Machine Learning Research, 2006, 7:2003-2030. [11] HOYER P O, JANZING D, MOOIJ J, et al. Nonlinear causal discovery with additive noise models[C]//Proceedings of the 21st International Conference on Neural Information Processing Systems. Red Hook, NY:Curran Associates Inc., 2008:689-696. [12] JANZING D, MOOIJ J, ZHANG K, et al. Information-geometric approach to inferring causal directions[J]. Artificial Intelligence, 2012, 182/183:1-31. [13] CAI R C, ZHANG Z J, HAO Z F. SADA:a general framework to support robust causation discovery[C]//Proceedings of the 30th International Conference on Machine Learning. New York:JMLR. org, 2013:208-216. [14] CAI R C, QIAO J, ZHANG Z J, et al. SELF:structural equational likelihood framework for causal discovery[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA:AAAI Press, 2018:1787-1794. [15] 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. [16] PETERS J, JANZING D, SCHOLKOPF B. Causal inference on discrete data using additive noise models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12):2436-2450. [17] COLOMBO D, MAATHUIS M H, KALISCH M, et al. Learning high-dimensional directed acyclic graphs with latent and selection variables[J]. The Annals of Statistics, 2012, 40(1):294-321. [18] PEARL J On the testability of causal models with latent and instrumental variables[C]//Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. San Francisco:Morgan Kaufmann Publishers Inc., 1995:435-443. [19] GLYMOUR M M, TCHETGEN TCHETGEN E J, ROBINS J M. Credible mendelian randomization studies:approaches for evaluating the instrumental variable assumptions[J]. American Journal of Epidemiology, 2012, 175(4):332-339. [20] TASHIRO T, SHIMIZU S, HYVÄRINEN A, et al. ParceLiNGAM:a causal ordering method robust against latent confounders[J]. Neural Computation, 2014, 26(1):57-83. [21] HOYER P O, SHIMIZU S, KERMINEN A J, et al. Estimation of causal effects using linear non-Gaussian causal models with hidden variables[J]. International Journal of Approximate Reasoning, 2008, 49(2):362-378. [22] CHAVES R, LUFT L, MACIEL T O, et al. Inferring latent structures via information inequalities[C]//Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence. Arlington, VA:AUAI Press, 2014:112-121. [23] CHICKERING D M. Optimal structure identification with greedy search[J]. Journal of Machine Learning Research, 2002, 3:507-554. [24] GRETTON A, BOUSQUET O, SMOLA A, et al. Measuring statistical dependence with Hilbert-Schmidt norms[C]//Proceedings of the 2005 International Conference on Algorithmic Learning Theory, LNCS 3734. Berlin:Springer, 2005:63-77. [25] KALISCH M, MÄCHLER M, COLOMBO D, et al. Causal inference using graphical models with the R package pcalg[J]. Journal of Statistical Software, 2012, 47(11):1-26. [26] SCUTARI M. Learning Bayesian networks with the bnlearn R package[J]. Journal of Statistical Software, 2010, 35(3):1-22. [27] DUA D, GRAFF C. UCI machine learning repository[DB/OL].[2020-11-20]. http://archive.ics.uci.edu/ml. [28] MOOIJ J M, PETERS J, JANZING D, et al. Distinguishing cause from effect using observational data:methods and benchmarks[J]. Journal of Machine Learning Research, 2016, 17:1-102. |