{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:27:06Z","timestamp":1743082026641,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030362034"},{"type":"electronic","value":"9783030362041"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-36204-1_32","type":"book-chapter","created":{"date-parts":[[2019,11,28]],"date-time":"2019-11-28T14:03:54Z","timestamp":1574949834000},"page":"381-393","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples"],"prefix":"10.1007","author":[{"given":"Feng","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruichu","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,29]]},"reference":[{"key":"32_CR1","unstructured":"Spirtes, P., Glymour, C., Scheines, R., Tillman, R.: Automated search for causal relations: theory and practice (2010)"},{"issue":"3","key":"32_CR2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1145\/3241036","volume":"62","author":"J Pearl","year":"2019","unstructured":"Pearl, J.: The seven tools of causal inference, with reflections on machine learning. Commun. ACM 62(3), 54\u201360 (2019)","journal-title":"Commun. ACM"},{"key":"32_CR3","first-page":"125","volume":"8","author":"X Lele","year":"2014","unstructured":"Lele, X., et al.: A pooling-lingam algorithm for effective connectivity analysis of fMRI data. Front. Comput. Neurosci. 8, 125 (2014)","journal-title":"Front. Comput. Neurosci."},{"issue":"2","key":"32_CR4","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1162\/netn_a_00061","volume":"3","author":"R Sanchez-Romero","year":"2019","unstructured":"Sanchez-Romero, R., Ramsey, J.D., Zhang, K., Glymour, M.R.K., Huang, B., Glymour, C.: Estimating feedforward and feedback effective connections from fMRI time series: assessments of statistical methods. Netw. Neurosci. 3(2), 274\u2013306 (2019)","journal-title":"Netw. Neurosci."},{"issue":"Nov","key":"32_CR5","first-page":"2455","volume":"9","author":"K Zhang","year":"2008","unstructured":"Zhang, K., Chan, L.: Minimal nonlinear distortion principle for nonlinear independent component analysis. J. Mach. Learn. Res. 9(Nov), 2455\u20132487 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"32_CR6","doi-asserted-by":"publisher","first-page":"120950","DOI":"10.1016\/j.physa.2019.04.186","volume":"531","author":"KH Al-Yahyaee","year":"2019","unstructured":"Al-Yahyaee, K.H., Mensi, W., Al-Jarrah, I.M.W., Tiwari, A.K.: Testing for the Granger-causality between returns in the U.S and GIPSI stock markets. Phys. A: Stat. Mech. Appl. 531, 120950 (2019)","journal-title":"Phys. A: Stat. Mech. Appl."},{"issue":"8","key":"32_CR7","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1109\/TNNLS.2016.2556724","volume":"28","author":"R Cai","year":"2016","unstructured":"Cai, R., Zhang, Z., Hao, Z., Winslett, M.: Understanding social causalities behind human action sequences. IEEE Trans. Neural Netw. Learn. Syst. 28(8), 1801\u20131813 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"7","key":"32_CR8","doi-asserted-by":"publisher","first-page":"e101860","DOI":"10.1371\/journal.pone.0101860","volume":"9","author":"H Helaj\u00e4rvi","year":"2014","unstructured":"Helaj\u00e4rvi, H., Rosenstr\u00f6m, T., et al.: Exploring causality between TV viewing and weight change in young and middle-aged adults. The cardiovascular risk in young finns study. PLoS One 9(7), e101860 (2014)","journal-title":"PLoS One"},{"issue":"1","key":"32_CR9","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1177\/089443939100900106","volume":"9","author":"P Spirtes","year":"1991","unstructured":"Spirtes, P., Glymour, C.: An algorithm for fast recovery of sparse causal graphs. Soc. Sci. Comput. Rev. 9(1), 62\u201372 (1991)","journal-title":"Soc. Sci. Comput. Rev."},{"key":"32_CR10","unstructured":"Pearl, J., Verma, T.: A theory of inferred causation. In: Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning, KR 1991, pp. 441\u2013452 (1991)"},{"issue":"Oct","key":"32_CR11","first-page":"2003","volume":"7","author":"S Shimizu","year":"2006","unstructured":"Shimizu, S., Hoyer, P.O., Hyv\u00e4rinen, A., Kerminen, A.: A linear non-Gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7(Oct), 2003\u20132030 (2006)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"32_CR12","doi-asserted-by":"publisher","first-page":"65","DOI":"10.2333\/bhmk.41.65","volume":"41","author":"S Shimizu","year":"2014","unstructured":"Shimizu, S.: LiNGAM: non-Gaussian methods for estimating causal structures. Behaviormetrika 41(1), 65\u201398 (2014)","journal-title":"Behaviormetrika"},{"key":"32_CR13","first-page":"1","volume":"20","author":"S Shimizu","year":"2018","unstructured":"Shimizu, S.: Non-gaussian methods for causal structure learning. Prev. Sci. 20, 1\u201311 (2018)","journal-title":"Prev. Sci."},{"key":"32_CR14","unstructured":"Hoyer, P.O., Hyttinen, A.: Bayesian discovery of linear acyclic causal models. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 240\u2013248. AUAI Press (2009)"},{"issue":"Apr","key":"32_CR15","first-page":"1225","volume":"12","author":"S Shimizu","year":"2011","unstructured":"Shimizu, S., et al.: DirectLiNGAM: a direct method for learning a linear non-Gaussian structural equation model. J. Mach. Learn. Res. 12(Apr), 1225\u20131248 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"Jan","key":"32_CR16","first-page":"111","volume":"14","author":"A Hyv\u00e4rinen","year":"2013","unstructured":"Hyv\u00e4rinen, A., Smith, S.M.: Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. J. Mach. Learn. Res. 14(Jan), 111\u2013152 (2013)","journal-title":"J. Mach. Learn. Res."},{"key":"32_CR17","unstructured":"Kagan, A.M., Rao, C.R., Linnik, Y.V.: Characterization problems in mathematical statistics (1973)"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhou, S., Zhang, K., Guan, J.: Causal discovery using regression-based conditional independence tests. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10698"},{"key":"32_CR19","volume-title":"Causation, Prediction, and Search","author":"P Spirtes","year":"2000","unstructured":"Spirtes, P., Glymour, C.N., Scheines, R.: Causation, Prediction, and Search. MIT Press, Cambridge (2000)"},{"key":"32_CR20","volume-title":"Causality: Models, Reasoning and Inference","author":"J Pearl","year":"2000","unstructured":"Pearl, J.: Causality: Models, Reasoning and Inference, vol. 29. Springer, Heidelberg (2000)"},{"issue":"8","key":"32_CR21","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1016\/j.neunet.2011.05.017","volume":"24","author":"Y Sogawa","year":"2011","unstructured":"Sogawa, Y., Shimizu, S., Shimamura, T., Hyv\u00e4Rinen, A., Washio, T., Imoto, S.: Estimating exogenous variables in data with more variables than observations. Neural Netw. 24(8), 875\u2013880 (2011)","journal-title":"Neural Netw."},{"key":"32_CR22","unstructured":"Cai, R., Zhang, Z., Hao, Z.: SADA: a general framework to support robust causation discovery. In: International Conference on Machine Learning, pp. 208\u2013216 (2013)"},{"issue":"May","key":"32_CR23","first-page":"1709","volume":"11","author":"A Hyv\u00e4rinen","year":"2010","unstructured":"Hyv\u00e4rinen, A., Zhang, K., Shimizu, S., Hoyer, P.O.: Estimation of a structural vector autoregression model using non-gaussianity. J. Mach. Learn. Res. 11(May), 1709\u20131731 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"32_CR24","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.ijar.2008.02.006","volume":"49","author":"PO Hoyer","year":"2008","unstructured":"Hoyer, P.O., Shimizu, S., Kerminen, A.J., Palviainen, M.: Estimation of causal effects using linear non-gaussian causal models with hidden variables. Int. J. Approx. Reason. 49(2), 362\u2013378 (2008)","journal-title":"Int. J. Approx. Reason."},{"issue":"1","key":"32_CR25","first-page":"2629","volume":"15","author":"S Shimizu","year":"2014","unstructured":"Shimizu, S., Bollen, K.: Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. J. Mach. Learn. Res. 15(1), 2629\u20132652 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"32_CR26","first-page":"3065","volume":"15","author":"P-L Loh","year":"2014","unstructured":"Loh, P.-L., B\u00fchlmann, P.: High-dimensional learning of linear causal networks via inverse covariance estimation. J. Mach. Learn. Res. 15(1), 3065\u20133105 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Cai, R., Xie, F., Chen, W., Hao, Z.: An efficient kurtosis-based causal discovery method for linear non-Gaussian acyclic data. In 2017 IEEE\/ACM 25th International Symposium on Quality of Service, pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/IWQoS.2017.7969175"},{"key":"32_CR28","unstructured":"Hoyer, P.O., et al.: Causal discovery of linear acyclic models with arbitrary distributions. In: Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, pp. 282\u2013289. AUAI Press (2008)"}],"container-title":["Lecture Notes in Computer Science","Intelligence Science and Big Data Engineering. Big Data and Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-36204-1_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T04:02:39Z","timestamp":1665115359000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-36204-1_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030362034","9783030362041"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-36204-1_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"29 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IScIDE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Science and Big Data Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iscide2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iscide.njust.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}