{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:41:25Z","timestamp":1757310085337,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031205026"},{"type":"electronic","value":"9783031205033"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20503-3_4","type":"book-chapter","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T12:09:06Z","timestamp":1671192546000},"page":"39-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Genetic Algorithm for\u00a0Causal Discovery Based on\u00a0Structural Causal Model"],"prefix":"10.1007","author":[{"given":"Zhengyin","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenpin","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,17]]},"reference":[{"issue":"6","key":"4_CR1","first-page":"1470","volume":"40","author":"RC Cai","year":"2017","unstructured":"Cai, R.C., Chen, W., Zhang, K., Hao, Z.F.: A survey on non-temporal series observational data based causal discovery (in Chinese). Chin. J. Comput. 40(6), 1470\u20131490 (2017)","journal-title":"Chin. J. Comput."},{"key":"4_CR2","unstructured":"Cai, R.C., Hao, Z.F.: Casual discovery in big data (in Chinese). Science Press (2018)"},{"key":"4_CR3","doi-asserted-by":"publisher","unstructured":"Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Fisher, D., Lenz, H.J. (eds.) Learning from Data. Lecture Notes in Statistics, vol. 112, pp. 121\u2013130. Springer, New York, NY (1996). https:\/\/doi.org\/10.1007\/978-1-4612-2404-4_12","DOI":"10.1007\/978-1-4612-2404-4_12"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Chickering, D.M.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3(3), 507\u2013554 (2002). https:\/\/doi.org\/10.1162\/153244303321897717","DOI":"10.1162\/153244303321897717"},{"key":"4_CR5","first-page":"1287","volume":"5","author":"M Chickering","year":"2004","unstructured":"Chickering, M., Heckerman, D., Meek, C.: Large-sample learning of Bayesian networks is NP-hard. J. Mach. Learn. Res. 5, 1287\u20131330 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"524","DOI":"10.3389\/fgene.2019.00524","volume":"10","author":"C Glymour","year":"2019","unstructured":"Glymour, C., Zhang, K., Spirtes, P.: Review of causal discovery methods based on graphical models. Front. Genet. 10, 524 (2019). https:\/\/doi.org\/10.3389\/fgene.2019.00524","journal-title":"Front. Genet."},{"key":"4_CR7","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-319-98131-4_3","volume-title":"Explainable and Interpretable Models in Computer Vision and Machine Learning","author":"O Goudet","year":"2018","unstructured":"Goudet, O., Kalainathan, D., Caillou, P., Guyon, I., Lopez-Paz, D., Sebag, M.: Learning functional causal models with generative neural networks. In: Escalante, H.J., et al. (eds.) Explainable and Interpretable Models in Computer Vision and Machine Learning. TSSCML, pp. 39\u201380. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98131-4_3"},{"key":"4_CR8","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1007\/s10846-005-9001-9","volume":"6","author":"A Gretton","year":"2005","unstructured":"Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Sch\u00f6lkopf, B., et al.: Kernel methods for measuring independence. J. Mach. Learn. Res. 6, 2075\u20132129 (2005). https:\/\/doi.org\/10.1007\/s10846-005-9001-9","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Haughton, D.M.: On the choice of a model to fit data from an exponential family. Ann. Stat. 16, pp. 342\u2013355 (1988). https:\/\/doi.org\/10.1214\/aos\/1176350709","DOI":"10.1214\/aos\/1176350709"},{"key":"4_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/11564089_9","volume-title":"Algorithmic Learning Theory","author":"Y-B He","year":"2005","unstructured":"He, Y.-B., Geng, Z., Liang, X.: Learning causal structures based on Markov equivalence class. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 92\u2013106. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564089_9"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"He, Y., Cui, P., Shen, Z., Xu, R., Liu, F., Jiang, Y.: Daring: differentiable causal discovery with residual independence. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery Data Mining, pp. 596\u2013605 (2021). https:\/\/doi.org\/10.1145\/3447548.3467439","DOI":"10.1145\/3447548.3467439"},{"key":"4_CR12","unstructured":"Hoyer, P., Janzing, D., Mooij, J.M., Peters, J., Sch\u00f6lkopf, B.: Nonlinear causal discovery with additive noise models. In: Advances in Neural Information Processing Systems 21 (2008)"},{"key":"4_CR13","unstructured":"Kitson, N.K., Constantinou, A.C., Guo, Z., Liu, Y., Chobtham, K.: A survey of Bayesian network structure learning. arXiv preprint arXiv:2109.11415 (2021)"},{"key":"4_CR14","unstructured":"Lachapelle, S., Brouillard, P., Deleu, T., Lacoste-Julien, S.: Gradient-based neural DAG learning. In: International Conference on Learning Representations (2020)"},{"issue":"7433","key":"4_CR15","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1038\/493473a","volume":"493","author":"CA Mattmann","year":"2013","unstructured":"Mattmann, C.A.: A vision for data science. Nature 493(7433), 473\u2013475 (2013). https:\/\/doi.org\/10.1038\/493473a","journal-title":"Nature"},{"key":"4_CR16","unstructured":"Peters, J., Janzing, D., Sch\u00f6lkopf, B.: Elements of causal inference: foundations and learning algorithms. The MIT Press (2017)"},{"key":"4_CR17","unstructured":"Peters, J., Mooij, J.M., Janzing, D., Sch\u00f6lkopf, B.: Causal discovery with continuous additive noise models. J. Mach. Learn. Res. 15(58), 2009\u20132053 (2014). http:\/\/hdl.handle.net\/2066\/130001"},{"issue":"5721","key":"4_CR18","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1126\/science.1105809","volume":"308","author":"K Sachs","year":"2005","unstructured":"Sachs, K., Perez, O., Pe\u2019er, D., Lauffenburger, D.A., Nolan, G.P.: Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721), 523\u2013529 (2005). https:\/\/doi.org\/10.1126\/science.1105809","journal-title":"Science"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Shimizu, S., Hoyer, P.O., Hyv\u00e4rinen, A., Kerminen, A., Jordan, M.: A linear non-gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7(10), 2003\u20132030 (2006). https:\/\/doi.org\/10.1007\/s10883-006-0005-y","DOI":"10.1007\/s10883-006-0005-y"},{"key":"4_CR20","unstructured":"Spirtes, P., Glymour, C., Scheines, R.: Causality from probability (1989)"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Spirtes, P., Glymour, C.N., Scheines, R., Heckerman, D.: Causation, prediction, and search. MIT press (2000)","DOI":"10.7551\/mitpress\/1754.001.0001"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Verma, T.S., Pearl, J.: Equivalence and synthesis of causal models. In: Probabilistic and Causal Inference: the works of Judea Pearl, pp. 221\u2013236 (2022). https:\/\/doi.org\/10.1145\/3501714.3501732","DOI":"10.1145\/3501714.3501732"},{"key":"4_CR23","unstructured":"Wang, X., Dunson, D., Leng, C.: No penalty no tears: least squares in high-dimensional linear models. In: International Conference on Machine Learning, pp. 1814\u20131822. PMLR (2016)"},{"key":"4_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1007\/978-3-642-04174-7_37","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"K Zhang","year":"2009","unstructured":"Zhang, K., Hyv\u00e4rinen, A.: Causality discovery with additive disturbances: an information-theoretical perspective. In: Buntine, W., Grobelnik, M., Mladeni\u0107, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 570\u2013585. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04174-7_37"},{"key":"4_CR25","unstructured":"Zhang, K., Hyv\u00e4rinen, A.: Distinguishing causes from effects using nonlinear acyclic causal models. In: Causality: Objectives and Assessment, pp. 157\u2013164. PMLR (2010)"},{"key":"4_CR26","unstructured":"Zhang, K., Hyvarinen, A.: On the identifiability of the post-nonlinear causal model. arXiv preprint arXiv:1205.2599 (2012)"},{"key":"4_CR27","unstructured":"Zheng, X., Aragam, B., Ravikumar, P.K., Xing, E.P.: DAGs with no tears: continuous optimization for structure learning. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"4_CR28","unstructured":"Zhou, S.: Thresholding procedures for high dimensional variable selection and statistical estimation. In: Advances in Neural Information Processing Systems 22 (2009)"},{"key":"4_CR29","unstructured":"Zhu, S., Ng, I., Chen, Z.: Causal discovery with reinforcement learning. In: International Conference on Learning Representations (2020)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20503-3_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T12:28:33Z","timestamp":1671193713000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20503-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031205026","9783031205033"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20503-3_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"472","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}