{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T15:11:29Z","timestamp":1760800289770,"version":"3.37.3"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030218096"},{"type":"electronic","value":"9783030218102"}],"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\/978-3-030-21810-2_2","type":"book-chapter","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:26:46Z","timestamp":1571790406000},"page":"27-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluation Methods of Cause-Effect Pairs"],"prefix":"10.1007","author":[{"given":"Isabelle","family":"Guyon","sequence":"first","affiliation":[]},{"given":"Olivier","family":"Goudet","sequence":"additional","affiliation":[]},{"given":"Diviyan","family":"Kalainathan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,23]]},"reference":[{"key":"2_CR1","unstructured":"Constantin F. Aliferis, Ioannis Tsamardinos, Alexander R. Statnikov, and Laura E. Brown. Causal explorer: A causal probabilistic network learning toolkit for biomedical discovery. In Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS \u201903, June 23 - 26, 2003, Las Vegas, Nevada, USA, pages 371\u2013376, 2003."},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Demian Battaglia, Isabelle Guyon, Vincent Lemaire, Javier Orlandi, Bisakha Ray, and Jordi Soriano. Neural Connectomics Challenge. Springer Publishing Company, Incorporated, 1st edition, 2017. ISBN 3319530690, 9783319530697.","DOI":"10.1007\/978-3-319-53070-3"},{"issue":"3","key":"2_CR3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/S0168-1699(99)00046-0","volume":"24","author":"Jock A. Blackard","year":"1999","unstructured":"J A Blackard and D J Dean. Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture, vol.24:131\u2013151, 1999.","journal-title":"Computers and Electronics in Agriculture"},{"key":"2_CR4","unstructured":"Patrick Bloebaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, and Bernhard Schoelkopf. Cause-effect inference by comparing regression errors. In International Conference on Artificial Intelligence and Statistics, pages 900\u2013909, 2018."},{"key":"2_CR5","first-page":"3","volume":"8","author":"C Bonferroni","year":"1936","unstructured":"C Bonferroni. Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze, 8:3\u201362, 1936.","journal-title":"Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze"},{"key":"2_CR6","unstructured":"Gianluca Bontempi. From dependency to causality: a machine learning approach. In Proc. NIPS 2013 workshop on causality, \n                    http:\/\/clopinet.com\/isabelle\/Projects\/NIPS2013\/\n                    \n                  , December 2013."},{"issue":"1","key":"2_CR7","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1097\/PRS.0b013e318219c171","volume":"128","author":"P B Burns","year":"2011","unstructured":"P. B. Burns, R. J. Rohrich, and K. C. Chung. The levels of evidence and their role in evidence-based medicine. Plastic and reconstructive surgery, 128(1):305\u2013310, 2011.","journal-title":"Plastic and reconstructive surgery"},{"key":"2_CR8","unstructured":"Krzysztof Chalupka, Frederick Eberhardt, and Pietro Perona. Estimating causal direction and confounding of two discrete variables. arXiv preprint arXiv:1611.01504, 2016."},{"key":"2_CR9","unstructured":"Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, and Bernhard Sch\u00f6lkopf. Inferring deterministic causal relations. arXiv preprint arXiv:1203.3475, 2012."},{"key":"2_CR10","unstructured":"Diogo Moitinho de Almeida. Automated feature engineering applied to causality. In Proc. NIPS 2013 workshop on causality, \n                    http:\/\/clopinet.com\/isabelle\/Projects\/NIPS2013\/\n                    \n                  , December 2013."},{"issue":"6","key":"2_CR11","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1093\/bib\/bbt051","volume":"15","author":"S. de Siqueira Santos","year":"2013","unstructured":"Suzana de Siqueira Santos, Daniel Yasumasa Takahashi, Asuka Nakata, and Andr\u00e9 Fujita. A comparative study of statistical methods used to identify dependencies between gene expression signals. Briefings in Bioinformatics, 15(6):906\u2013918, 2014. doi: 10.1093\/bib\/bbt051. URL \n                    http:\/\/dx.doi.org\/10.1093\/bib\/bbt051\n                    \n                  .","journal-title":"Briefings in Bioinformatics"},{"key":"2_CR12","volume-title":"Pattern Classification","author":"R O Duda","year":"2001","unstructured":"R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley &amp; Sons, USA, 2nd edition, 2001.","edition":"2"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1080\/01621459.1983.10477973","volume":"78","author":"B Efron","year":"1983","unstructured":"B. Efron. Estimating the error rate of a prediction rule: Improvement on cross-validation. Journal of the American Statistical Association, 78:316\u2013331, 1983.","journal-title":"Journal of the American Statistical Association"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Bradley Efron and Robert J Tibshirani. An introduction to the bootstrap. CRC press, 1994.","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Guy Fagherazzi, Alice Vilier, Daniela Saes Sartorelli, Martin Lajous, Beverley Balkau, and Fran\u00e7oise Clavel-Chapelon. Consumption of artificially and sugar-sweetened beverages and incident type 2 diabetes in the etude epid\u00e9miologique aupr\u00e8s des femmes de la mutuelle g\u00e9n\u00e9rale de l\u2019education nationale\u2013european prospective investigation into cancer and nutrition cohort. The American Journal of Clinical Nutrition, 97(3):517\u2013523, March 2013.","DOI":"10.3945\/ajcn.112.050997"},{"key":"2_CR16","unstructured":"Ronald A. Fisher. The Design of Experiments. 1935."},{"key":"2_CR17","unstructured":"Jos\u00e9 A. R. Fonollosa. Conditional distribution variability measure for causality detection. In Proc. NIPS 2013 workshop on causality, \n                    http:\/\/clopinet.com\/isabelle\/Projects\/NIPS2013\/\n                    \n                  , December 2013."},{"key":"2_CR18","unstructured":"Jos\u00e9 AR Fonollosa. Conditional distribution variability measures for causality detection. arXiv preprint arXiv:1601.06680, 2016."},{"key":"2_CR19","unstructured":"U.S. Preventive Services Task Force. Guide to clinical preventive services: report of the u.s. preventive services task force. 1989."},{"issue":"1","key":"2_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/neco.1992.4.1.1","volume":"4","author":"Stuart Geman","year":"1992","unstructured":"Stuart Geman, Elie Bienenstock, and Ren\u00e9 Doursat. Neural networks and the bias\/variance dilemma. Neural Comput., 4(1):1\u201358, 1992. ISSN 0899-7667. doi: \n                    https:\/\/doi.org\/10.1162\/neco.1992.4.1.1\n                    \n                  .","journal-title":"Neural Computation"},{"key":"2_CR21","unstructured":"Olivier Goudet. Causality pairwise inference datasets. replication data for: \u201clearning functional causal models with generative neural networks\u201d, 2017. URL \n                    http:\/\/dx.doi.org\/10.7910\/DVN\/3757KX\n                    \n                  ."},{"key":"2_CR22","unstructured":"Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Mich\u00e8le Sebag. Causal generative neural networks. arXiv preprint arXiv:1711.08936, 2017."},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Clive WJ Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, pages 424\u2013438, 1969.","DOI":"10.2307\/1912791"},{"key":"2_CR24","unstructured":"A. Gretton, K. Fukumizu, CH. Teo, L. Song, B. Sch\u00f6lkopf, and AJ. Smola. A kernel statistical test of independence. In Advances in neural information processing systems 20, pages 585\u2013592, Red Hook, NY, USA, September 2008. Max-Planck-Gesellschaft, Curran."},{"issue":"Dec","key":"2_CR25","first-page":"2075","volume":"6","author":"Arthur Gretton","year":"2005","unstructured":"Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, and Bernhard Sch\u00f6lkopf. Kernel methods for measuring independence. Journal of Machine Learning Research, 6(Dec):2075\u20132129, 2005.","journal-title":"Journal of Machine Learning Research"},{"key":"2_CR26","unstructured":"I. Guyon, C. Aliferis, G. Cooper, A. Elisseeff, J.-P. Pellet, P. Spirtes, and A. Statnikov. Design and analysis of the causality pot-luck challenge. In JMLR W&CP, volume 5: NIPS 2008 causality workshop, Whistler, Canada, December 12 2008."},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"I. Guyon, D. Battaglia, A. Guyon, V. Lemaire, J. G. Orlandi, B. Ray, M. Saeed, J. Soriano, A. Statnikov, and O. Stetter. Design of the first neuronal connectomics challenge: From imaging to connectivity. In 2014 International Joint Conference on Neural Networks (IJCNN), pages 2600\u20132607, July 2014. \n                    https:\/\/doi.org\/10.1109\/IJCNN.2014.6889913\n                    \n                  .","DOI":"10.1109\/IJCNN.2014.6889913"},{"key":"2_CR28","unstructured":"Isabelle Guyon. A practical guide to model selection, 2010."},{"key":"2_CR29","unstructured":"Isabelle Guyon. Chalearn cause effect pairs challenge, 2013. URL \n                    http:\/\/www.causality.inf.ethz.ch\/cause-effect.php\n                    \n                  ."},{"key":"2_CR30","unstructured":"Isabelle Guyon. Chalearn fast causation coefficient challenge. 2014."},{"key":"2_CR31","unstructured":"Isabelle Guyon and et al. Results and analysis of the 2013 chalearn cause-effect pair challenge. In Proc. NIPS 2013 workshop on causality, Workshop URL: \n                    http:\/\/clopinet.com\/isabelle\/Projects\/NIPS2013\/\n                    \n                  ; Challenge URL: \n                    http:\/\/www.causality.inf.ethz.ch\/cause-effect.php\n                    \n                  , December 2013."},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lotfi A. Zadeh. Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer-Verlag, Berlin, Heidelberg, 2006. ISBN 3540354875.","DOI":"10.1007\/978-3-540-35488-8"},{"key":"2_CR33","unstructured":"Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Sch\u00f6lkopf. Nonlinear causal discovery with additive noise models. In Neural Information Processing Systems (NIPS), pages 689\u2013696, 2009."},{"key":"2_CR34","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199574131.001.0001","volume-title":"Causality in the Sciences","author":"P Illari","year":"2011","unstructured":"P. Illari, F. Russo, and J. Williamson. Causality in the Sciences. Oxford University Press, 2011."},{"issue":"10","key":"2_CR35","doi-asserted-by":"publisher","first-page":"5168","DOI":"10.1109\/TIT.2010.2060095","volume":"56","author":"Dominik Janzing","year":"2010","unstructured":"Dominik Janzing and Bernhard Scholkopf. Causal inference using the algorithmic markov condition. IEEE Transactions on Information Theory, 56(10):5168\u20135194, 2010.","journal-title":"IEEE Transactions on Information Theory"},{"key":"2_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2012.01.002. URL","volume":"182\u2013183","author":"Dominik Janzing","year":"2012","unstructured":"Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniu\u0161is, Bastian Steudel, and Bernhard Sch\u00f6lkopf. Information-geometric approach to inferring causal directions. Artif. Intell., 182-183:1\u201331, May 2012. ISSN 0004-3702. doi: 10.1016\/j.artint.2012.01.002. URL \n                    http:\/\/dx.doi.org\/10.1016\/j.artint.2012.01.002\n                    \n                  .","journal-title":"Artif. Intell."},{"key":"2_CR37","unstructured":"Mingyu Chung Gabriela K. Fragiadakis Jonathan Fitzgerald Birgit Schoeberl Garry P. Nolan Claire Tomlin Karen Sachs, Solomon Itani. Experiment design in static models of dynamic biological systems. In NIPS2013 workshop on causality, 2013."},{"key":"2_CR38","unstructured":"Shachar Kaufman, Saharon Rosset, Claudia Perlich, and Ori Stitelman. Leakage in data mining: Formulation, detection, and avoidance. ACM Transactions on Knowledge Discovery from Data (TKDD), 6(4):15, 2012."},{"key":"2_CR39","unstructured":"Ron Kohavi. Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid. In PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, pages 202\u2013207. AAAI Press, 1996."},{"key":"2_CR40","unstructured":"S. Kpotufe, E. Sgouritsa, D. Janzing, and B. Sch\u00f6lkopf. Consistency of causal inference under the additive noise model. In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages 478\u2013495. JMLR, 2014."},{"key":"2_CR41","unstructured":"John Langford. Tutorial on practical prediction theory for classification. J. Mach. Learn. Res., 6:273\u2013306, December 2005. ISSN 1532-4435. URL \n                    http:\/\/dl.acm.org\/citation.cfm?id=1046920.1058111\n                    \n                  ."},{"key":"2_CR42","unstructured":"David Lopez-Paz, Krikamol Muandet, and Benjamin Recht. The randomized causation coefficient. J. Mach. Learn. Res., 16(1):2901\u20132907, January 2015a. ISSN 1532-4435. URL \n                    http:\/\/dl.acm.org\/citation.cfm?id=2789272.2912092\n                    \n                  ."},{"key":"2_CR43","unstructured":"David Lopez-Paz, Krikamol Muandet, Bernhard Sch\u00f6lkopf, and Ilya O Tolstikhin. Towards a learning theory of cause-effect inference. In ICML, pages 1452\u20131461, 2015b."},{"key":"2_CR44","unstructured":"David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Sch\u00f6lkopf, and L\u00e9on Bottou. Discovering causal signals in images. arXiv preprint arXiv:1605.08179;, 2016."},{"key":"2_CR45","unstructured":"Bram Minnaert. Feature importance in causal inference for numerical and categorical variables. In Proc. NIPS 2013 workshop on causality, \n                    http:\/\/clopinet.com\/isabelle\/Projects\/NIPS2013\/\n                    \n                  , December 2013."},{"key":"2_CR46","unstructured":"Joris M Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Sch\u00f6lkopf. Distinguishing cause from effect using observational data: methods and benchmarks. Journal of Machine Learning Research, 17(32):1\u2013102, 2016."},{"key":"2_CR47","volume-title":"Causality: Models, Reasoning, and Inference","author":"J Pearl","year":"2000","unstructured":"J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000."},{"key":"2_CR48","volume-title":"Elements of Causal Inference: Foundations and Learning Algorithms","author":"J Peters","year":"2017","unstructured":"J. Peters, D. Janzing, and B. Sch\u00f6lkopf. Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press, Cambridge, MA, USA, 2017a."},{"issue":"12","key":"2_CR49","doi-asserted-by":"publisher","first-page":"2436","DOI":"10.1109\/TPAMI.2011.71","volume":"33","author":"Jonas Peters","year":"2011","unstructured":"Jonas Peters, Dominik Janzing, and Bernhard Scholkopf. Causal inference on discrete data using additive noise models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12):2436\u20132450, 2011.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2_CR50","volume-title":"Elements of causal inference: foundations and learning algorithms","author":"Jonas Peters","year":"2017","unstructured":"Jonas Peters, Dominik Janzing, and Bernhard Sch\u00f6lkopf. Elements of causal inference: foundations and learning algorithms. MIT press, 2017b."},{"key":"2_CR51","doi-asserted-by":"crossref","unstructured":"Karl Popper. Conjectures and refutations: The growth of scientific knowledge. routledge, 2014.","DOI":"10.4324\/9780203538074"},{"key":"2_CR52","doi-asserted-by":"publisher","DOI":"10.1063\/1.3059791","volume-title":"The direction of time","author":"Hans Reichenbach","year":"1956","unstructured":"Hans Reichenbach. The direction of time. Dover Publications, 1956."},{"issue":"1","key":"2_CR53","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1093\/biomet\/70.1.41","volume":"70","author":"Paul R Rosenbaum","year":"1983","unstructured":"Paul R. Rosenbaum and Donald B. Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1):41\u201355, 1983.","journal-title":"Biometrika"},{"issue":"5","key":"2_CR54","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1037\/h0037350","volume":"66","author":"Donald Rubin","year":"1974","unstructured":"Donald Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5):688\u2013701, 1974.","journal-title":"Journal of Educational Psychology"},{"key":"2_CR55","unstructured":"Spyridon Samothrakis, Diego Perez, and Simon Lucas. Training gradient boosting machines using curve fitting and information-theoretic features for causal direction detection. In Proc. NIPS 2013 workshop on causality, \n                    http:\/\/clopinet.com\/isabelle\/Projects\/NIPS2013\/\n                    \n                  , December 2013."},{"key":"2_CR56","doi-asserted-by":"crossref","unstructured":"K. F. Schulz, D. G. Altman, D. Moher, and for the CONSORT Group. Consort 2010 statement: updated guidelines for reporting parallel group randomised trials. Ann. Int. Med., 2010.","DOI":"10.1016\/j.ijsu.2010.09.006"},{"key":"2_CR57","volume-title":"Causation, Prediction, and Search","author":"P Spirtes","year":"2000","unstructured":"P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, Cambridge, Massachusetts, London, England, 2000."},{"key":"2_CR58","doi-asserted-by":"crossref","unstructured":"Alexander Statnikov, Mikael Henaff, Nikita I Lytkin, and Constantin F Aliferis. New methods for separating causes from effects in genomics data. BMC Genomics, 13, 2012a.","DOI":"10.1186\/1471-2164-13-S8-S22"},{"issue":"Suppl 8","key":"2_CR59","doi-asserted-by":"publisher","first-page":"S22","DOI":"10.1186\/1471-2164-13-S8-S22","volume":"13","author":"Alexander Statnikov","year":"2012","unstructured":"Alexander Statnikov, Mikael Henaff, Nikita I Lytkin, and Constantin F Aliferis. New methods for separating causes from effects in genomics data. BMC genomics, 13(8):S22, 2012b.","journal-title":"BMC Genomics"},{"key":"2_CR60","unstructured":"Alexander Statnikov, Sisi Ma, Mikael Henaff, Nikita Lytkin, Efstratios Efstathiadis, Eric R. Peskin, and Constantin F. Aliferis. Ultra-scalable and efficient methods for hybrid observational and experimental local causal pathway discovery. J. Mach. Learn. Res., 16(1):3219\u20133267, January 2015. ISSN 1532-4435. URL \n                    http:\/\/dl.acm.org\/citation.cfm?id=2789272.2912102\n                    \n                  ."},{"key":"2_CR61","unstructured":"Natasa Tagasovska, Thibault Vatter, and Val\u00e9rie Chavez-Demoulin. Nonparametric quantile-based causal discovery. arXiv preprint arXiv:1801.10579, 2018."},{"key":"2_CR62","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Evidence-based_medicine\n                    \n                  . Evidence-based medicine."},{"key":"2_CR63","doi-asserted-by":"crossref","unstructured":"Yi Wang, Yi Li andHongbao Cao, Momiao Xiong, Yin Yao Shugart, and Li Jin. Efficient test for nonlinear dependence of two continuous variables. BMC Bioinformatics, 16(260), 2015.","DOI":"10.1186\/s12859-015-0697-7"},{"key":"2_CR64","unstructured":"K. Zhang, B. Sch\u00f6lkopf, K. Muandet, and Z. Wang. Domain adaptation under target and conditional shift. In Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3), page 819\u2013827. JMLR, 2013."},{"key":"2_CR65","unstructured":"Kun Zhang. Causal learning and machine learning. In Antti Hyttinen, Joe Suzuki, and Brandon Malone, editors, Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, volume 73 of Proceedings of Machine Learning Research, pages 4\u20134. PMLR, 20\u201322 Sep 2017. URL \n                    http:\/\/proceedings.mlr.press\/v73\/zhang17a.html\n                    \n                  ."},{"key":"2_CR66","unstructured":"Kun Zhang and Aapo Hyv\u00e4rinen. On the identifiability of the post-nonlinear causal model. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pages 647\u2013655. AUAI Press, 2009."},{"issue":"2","key":"2_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2700476","volume":"7","author":"Kun Zhang","year":"2015","unstructured":"Kun Zhang, Zhikun Wang, Jiji Zhang, and Bernhard Sch\u00f6lkopf. On estimation of functional causal models: general results and application to the post-nonlinear causal model. ACM Transactions on Intelligent Systems and Technology (TIST), 7(2):13, 2016.","journal-title":"ACM Transactions on Intelligent Systems and Technology"}],"container-title":["The Springer Series on Challenges in Machine Learning","Cause Effect Pairs in Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-21810-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:40:35Z","timestamp":1571791235000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-21810-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030218096","9783030218102"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-21810-2_2","relation":{},"ISSN":["2520-131X","2520-1328"],"issn-type":[{"type":"print","value":"2520-131X"},{"type":"electronic","value":"2520-1328"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}