{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T09:55:23Z","timestamp":1775987723622,"version":"3.50.1"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030218096","type":"print"},{"value":"9783030218102","type":"electronic"}],"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_1","type":"book-chapter","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:26:46Z","timestamp":1571790406000},"page":"3-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions"],"prefix":"10.1007","author":[{"given":"Dominik","family":"Janzing","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,23]]},"reference":[{"key":"1_CR1","volume-title":"Causality","author":"J Pearl","year":"2000","unstructured":"J. Pearl. Causality. Cambridge University Press, 2000."},{"key":"1_CR2","volume-title":"Elements of Causal Inference \u2013 Foundations and Learning Algorithms","author":"J Peters","year":"2017","unstructured":"J. Peters, D. Janzing, and B. Sch\u00f6lkopf. Elements of Causal Inference \u2013 Foundations and Learning Algorithms. MIT Press, 2017."},{"key":"1_CR3","unstructured":"P. Hoyer, D. Janzing, J. Mooij, J. Peters, and B Sch\u00f6lkopf. Nonlinear causal discovery with additive noise models. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Proceedings of the conference Neural Information Processing Systems (NIPS) 2008, Vancouver, Canada, 2009. MIT Press."},{"key":"1_CR4","unstructured":"J. Peters, D. Janzing, and B. Sch\u00f6lkopf. Identifying cause and effect on discrete data using additive noise models. In Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 9, Chia Laguna, Sardinia, Italy, 2010."},{"key":"1_CR5","unstructured":"K. Zhang and A. Hyv\u00e4rinen. On the identifiability of the post-nonlinear causal model. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI \u201909, pages 647\u2013655, Arlington, Virginia, United States, 2009. AUAI Press."},{"key":"1_CR6","unstructured":"D. Lopez-Paz, K. Muandet, B. Sch\u00f6lkopf, and I. Tolstikhin. Towards a learning theory of cause-effect inference. In Proceedings of the 32nd International Conference on Machine Learning, volume 37 of JMLR Workshop and Conference Proceedings, page 1452\u20131461. JMLR, 2015."},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"A. Marx and J. Vreeken. Telling cause from effect using MDL-based local and global regression. In 2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18\u201321, 2017, pages 307\u2013316, 2017.","DOI":"10.1109\/ICDM.2017.40"},{"key":"1_CR8","unstructured":"P. Bloebaum, D. Janzing, T. Washio, S. Shmimizu, and B. Sch\u00f6lkopf. Cause-effect inference by comparing regression errors. In A. Storkey and F. Perez-Cruz, editors, Proceedings of the 21th International Conference on Artificial Intelligence and Statistics (AISTATS), volume 84, pages 900\u2013909. PMLR, 2018."},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"J. Song, S. Oyama, and M. Kurihara. Tell cause from effect: models and evaluation. International Journal of Data Science and Analytics, 2017.","DOI":"10.1007\/s41060-017-0063-0"},{"key":"1_CR10","unstructured":"D. Janzing, J. Peters, J. Mooij, and B. Sch\u00f6lkopf. Identifying latent confounders using additive noise models. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), 249\u2013257. (Eds.) A. Ng and J. Bilmes, AUAI Press, Corvallis, OR, USA, 2009."},{"key":"1_CR11","unstructured":"D. Janzing, E. Sgouritsa, O. Stegle, P. Peters, and B. Sch\u00f6lkopf. Detecting low-complexity unobserved causes. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011). \n                    http:\/\/uai.sis.pitt.edu\/papers\/11\/p383-janzing.pdf\n                    \n                  ."},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"D. Janzing and B. Sch\u00f6lkopf. Detecting confounding in multivariate linear models. Journal of Causal Inference, 6(1), 2017.","DOI":"10.1515\/jci-2017-0013"},{"key":"1_CR13","unstructured":"D. Janzing and B. Sch\u00f6lkopf. Detecting non-causal artifacts in multivariate linear regression models. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80, pages 2245\u20132253. PMLR, 2018. \n                    http:\/\/proceedings.mlr.press\/v80\/janzing18a\/janzing18a.pdf\n                    \n                  ."},{"key":"1_CR14","volume-title":"The logic of scientific discovery","author":"K Popper","year":"1959","unstructured":"K. Popper. The logic of scientific discovery. Routledge, London, 1959."},{"key":"1_CR15","unstructured":"TETRAD. The tetrad homepage. \n                    http:\/\/www.phil.cmu.edu\/projects\/tetrad\/\n                    \n                  ."},{"issue":"5","key":"1_CR16","doi-asserted-by":"publisher","first-page":"2324","DOI":"10.1214\/13-AOS1145","volume":"41","author":"D Janzing","year":"2013","unstructured":"D. Janzing, D. Balduzzi, M. Grosse-Wentrup, and B. Sch\u00f6lkopf. Quantifying causal influences. Annals of Statistics, 41(5):2324\u20132358, 2013.","journal-title":"Annals of Statistics"},{"issue":"32","key":"1_CR17","first-page":"1","volume":"17","author":"J Mooij","year":"2016","unstructured":"J. Mooij, J. Peters, D. Janzing, J. Zscheischler, and B. Sch\u00f6lkopf. Distinguishing cause from effect using observational data: methods and benchmarks. Journal of Machine Learning Research, 17(32):1\u2013102, 2016.","journal-title":"Journal of Machine Learning Research"},{"key":"1_CR18","unstructured":"Database with cause-effect pairs. \n                    https:\/\/webdav.tuebingen.mpg.de\/cause-effect\/\n                    \n                  . Copyright information for each cause-effect pair is contained in the respective description file."},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"D. Janzing. Statistical assymmeries between cause and effect. In R. Renner and S. Stupar, editors, Time in physics, volume Tutorials, Schools, and Workshops in the Mathematical Sciences. Birkh\u00e4user, Cham, pages 129\u2013139. Springer, 2017.","DOI":"10.1007\/978-3-319-68655-4_8"},{"key":"1_CR20","volume-title":"From microphysics to macrophysics, volume 1","author":"R Balian","year":"2007","unstructured":"R. Balian. From microphysics to macrophysics, volume 1. Springer, 2007."},{"key":"1_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-45475-5","volume-title":"From microphysics to macrophysics, volume 2","author":"R Balian","year":"1991","unstructured":"R. Balian. From microphysics to macrophysics, volume 2. Springer, 1991."},{"key":"1_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/aristotelian\/13.1.1","volume":"3","author":"B Russell","year":"1913","unstructured":"B. Russell. On the notion of cause. Proceedings of the Aristotelian Society, 3:1\u201326, 1912\u20131913.","journal-title":"Proceedings of the Aristotelian Society"},{"issue":"3","key":"1_CR23","doi-asserted-by":"publisher","first-page":"033002","DOI":"10.1088\/1367-2630\/17\/3\/033002","volume":"17","author":"Christopher J Wood","year":"2015","unstructured":"C. Wood and R. Spekkens. The lesson of causal discovery algorithms for quantum correlations: causal explanations of Bell-inequality violations require fine-tuning. New Journal of Physics, 17(3):033002, 2015.","journal-title":"New Journal of Physics"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"M. Pawlowski and V. Scarani. Information causality. In G. Chiribella and R. Spekkens, editors, Quantum Theory: Informational Foundations and Foils, pages 423\u2013438. Springer, 2016.","DOI":"10.1007\/978-94-017-7303-4_12"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"H. Barnum and A. Wilce. Post-classical probability theory. In R. Spekkens and G. Chiribella, editors, Quantum Theory: Informational Foundations and Foils, pages 367\u2013420. Springer, 2016.","DOI":"10.1007\/978-94-017-7303-4_11"},{"issue":"5","key":"1_CR26","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/nphys3266","volume":"11","author":"Katja Ried","year":"2015","unstructured":"K. Ried, M. Agnew, L. Vermeyden, D. Janzing, R. Spekkens, and K. Resch. A quantum advantage for inferring causal structure. Nature Physics, 11(5):414\u2013420, 05 2015.","journal-title":"Nature Physics"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"M. Leifer and R. Spekkens. Towards a formulation of quantum theory as a causally neutral theory of Bayesian inference. Phys Rev, A(88):052130, 2013.","DOI":"10.1103\/PhysRevA.88.052130"},{"key":"1_CR28","unstructured":"D. Schmied, K. Ried, and R. Spekkens. Why initial system-environment correlations do not imply the failure of complete positivity: a causal perspective. preprint, arXiv:1806.02381, 2018."},{"key":"1_CR29","unstructured":"B. Sch\u00f6lkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij. On causal and anticausal learning. In Langford J. and J. Pineau, editors, Proceedings of the 29th International Conference on Machine Learning (ICML), pages 1255\u20131262. ACM, 2012."},{"key":"1_CR30","unstructured":"K Zhang, B Sch\u00f6lkopf, Krikamol Muandet, and Z Wang. Domain adaptation under target and conditional shift. 30th International Conference on Machine Learning, ICML 2013, pages 1856\u20131864, 01 2013."},{"key":"1_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-2748-9","volume-title":"Causation, Prediction, and Search (Lecture notes in statistics)","author":"P Spirtes","year":"1993","unstructured":"P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search (Lecture notes in statistics). Springer-Verlag, New York, NY, 1993."},{"key":"1_CR32","unstructured":"J. Peters, J. Mooij, D. Janzing, and B. Sch\u00f6lkopf. Identifiability of causal graphs using functional models. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011). \n                    http:\/\/uai.sis.pitt.edu\/papers\/11\/p589-peters.pdf\n                    \n                  ."},{"issue":"3","key":"1_CR33","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1080\/02331888.2015.1060237","volume":"50","author":"C Nowzohour","year":"2016","unstructured":"C. Nowzohour and P. B\u00fchlmann. Score-based causal learning in additive noise models. Statistics, 50(3):471\u2013485, 2016.","journal-title":"Statistics"},{"key":"1_CR34","unstructured":"P. Daniusis, D. Janzing, J. M. Mooij, J. Zscheischler, B. Steudel, K. Zhang, and B. Sch\u00f6lkopf. Inferring deterministic causal relations. In Proceedings of the 26th Annual Conference on Uncertainty in Artificial Intelligence (UAI), pages 143\u2013150. AUAI Press, 2010."},{"key":"1_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2012.01.002","volume":"182\u2013183","author":"D Janzing","year":"2012","unstructured":"D. Janzing, J. Mooij, K. Zhang, J. Lemeire, J. Zscheischler, P. Daniu\u0161is, B. Steudel, and B. Sch\u00f6lkopf. Information-geometric approach to inferring causal directions. Artificial Intelligence, 182\u2013183:1\u201331, 2012.","journal-title":"Artificial Intelligence"},{"key":"1_CR36","unstructured":"J. Mooij, O. Stegle, D. Janzing, K. Zhang, and B. Sch\u00f6lkopf. Probabilistic latent variable models for distinguishing between cause and effect. In Advances in Neural Information Processing Systems 23 (NIPS*2010), pages 1687\u20131695, 2011."},{"key":"1_CR37","unstructured":"E. Sgouritsa, D. Janzing, P. Hennig, and B. Sch\u00f6lkopf. Inference of cause and effect with unsupervised inverse regression. In G. Lebanon and S. Vishwanathan, editors, Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR Workshop and Conference Proceedings, 2015."},{"key":"1_CR38","first-page":"479","volume":"06","author":"D Janzing","year":"2010","unstructured":"D. Janzing, P. Hoyer, and B. Sch\u00f6lkopf. Telling cause from effect based on high-dimensional observations. Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, 06:479\u2013486, 2010.","journal-title":"Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel"},{"key":"1_CR39","unstructured":"J. Zscheischler, D. Janzing, and K. Zhang. Testing whether linear equations are causal: A free probability theory approach. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), 2011. \n                    http:\/\/uai.sis.pitt.edu\/papers\/11\/p839-zscheischler.pdf\n                    \n                  ."},{"issue":"3","key":"1_CR40","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/1912791","volume":"37","author":"C W J Granger","year":"1969","unstructured":"C. W. J. Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3):424\u201338, July 1969.","journal-title":"Econometrica"},{"key":"1_CR41","unstructured":"N. Shajarisales, D. Janzing, B. Sch\u00f6lkopf, and M. Besserve. Telling cause from effect in deterministic linear dynamical systems. In Proceedings of the 32th International Conference on Machine Learning (ICML), pages 285\u2013294. Journal of Machine Learning Rearch, 2015."},{"key":"1_CR42","unstructured":"J. W. Comley and D. L. Dowe. General Bayesian networks and asymmetric languages. In Proceedings of the Hawaii International Conference on Statistics and Related fields, June 2003."},{"key":"1_CR43","unstructured":"X. Sun, D. Janzing, and B. Sch\u00f6lkopf. Causal inference by choosing graphs with most plausible Markov kernels. In Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics, pages 1\u201311, Fort Lauderdale, FL, 2006."},{"key":"1_CR44","unstructured":"D. Janzing, X. Sun, and B. Sch\u00f6lkopf. Distinguishing cause and effect via second order exponential models. \n                    http:\/\/arxiv.org\/abs\/0910.5561\n                    \n                  , 2009."},{"issue":"10","key":"1_CR45","doi-asserted-by":"publisher","first-page":"5168","DOI":"10.1109\/TIT.2010.2060095","volume":"56","author":"D Janzing","year":"2010","unstructured":"D. Janzing and B. Sch\u00f6lkopf. Causal inference using the algorithmic Markov condition. IEEE Transactions on Information Theory, 56(10):5168\u20135194, 2010.","journal-title":"IEEE Transactions on Information Theory"},{"issue":"2","key":"1_CR46","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s11023-012-9283-1","volume":"23","author":"Jan Lemeire","year":"2012","unstructured":"J. Lemeire and D. Janzing. Replacing causal faithfulness with algorithmic independence of conditionals. Minds and Machines, 23(2):227\u2013249, 7 2012.","journal-title":"Minds and Machines"},{"issue":"3","key":"1_CR47","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1145\/321892.321894","volume":"22","author":"G Chaitin","year":"1975","unstructured":"G. Chaitin. A theory of program size formally identical to information theory. J. Assoc. Comput. Mach., 22(3):329\u2013340, 1975.","journal-title":"J. Assoc. Comput. Mach."},{"key":"1_CR48","unstructured":"S. Kpotufe, E. Sgouritsa, D. Janzing, and B. Sch\u00f6lkopf. Consistency of causal inference under the additive noise model. In Eric P. Xing and Tony Jebara, editors, Proceedings of the 31st International Conference on Machine Learning (ICML), W&CP 32 (1), pages 478\u2013495. JMLR, 2014."},{"key":"1_CR49","unstructured":"X. Sun. Sch\u00e4tzen von Kausalstrukturen anhand der Plausibilit\u00e4t ihrer Markoff-Kerne, 2004. Diploma thesis (in German), Universit\u00e4t Karlsruhe (TH)."},{"key":"1_CR50","unstructured":"S. Hawking. A brief history of time. Bantam, 1990."},{"issue":"2","key":"1_CR51","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1142\/S1230161210000126","volume":"17","author":"D Janzing","year":"2010","unstructured":"D. Janzing and B. Steudel. Justifying additive-noise-based causal discovery via algorithmic information theory. Open Systems and Information Dynamics, 17(2):189\u2013212, 2010.","journal-title":"Open Systems and Information Dynamics"},{"key":"1_CR52","unstructured":"Y. Kano and S. Shimizu. Causal inference using nonnormality. In Proceedings of the International Symposium on Science of Modeling, the 30th Anniversary of the Information Criterion, pages 261\u2013270, Tokyo, Japan, 2003."},{"key":"1_CR53","doi-asserted-by":"publisher","DOI":"10.1063\/1.3059791","volume-title":"The direction of time","author":"H Reichenbach","year":"1956","unstructured":"H. Reichenbach. The direction of time. University of California Press, Berkeley, 1956."},{"key":"1_CR54","first-page":"211","volume":"2","author":"V Skitovic","year":"1962","unstructured":"V. Skitovic. Linear combinations of independent random variables and the normal distribution law. Select. Transl. Math. Stat. Probab., (2):211\u2013228, 1962.","journal-title":"Select. Transl. Math. Stat. Probab."},{"issue":"093052","key":"1_CR55","first-page":"1","volume":"18","author":"D Janzing","year":"2016","unstructured":"D. Janzing, R. Chaves, and B. Sch\u00f6lkopf. Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference. New Journal of Physics, 18(093052):1\u201313, 2016.","journal-title":"New Journal of Physics"},{"key":"1_CR56","doi-asserted-by":"crossref","unstructured":"J. Peters, D. Janzing, A. Gretton, and B. Sch\u00f6lkopf. Detecting the direction of causal time series. In A Danyluk, L Bottou, and ML Littman, editors, Proceedings of the 26th International Conference on Machine Learning, pages 801\u2013808, New York, NY, USA, 2009. ACM Press.","DOI":"10.1145\/1553374.1553477"},{"key":"1_CR57","unstructured":"D. Janzing. On causally asymmetric versions of Occam\u2019s Razor and their relation to thermodynamics. \n                    http:\/\/arxiv.org\/abs\/0708.3411v2\n                    \n                  , 2008."},{"key":"1_CR58","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s10955-009-9897-8","volume":"138","author":"D Janzing","year":"2010","unstructured":"D. Janzing. On the entropy production of time series with unidirectional linearity. Journ. Stat. Phys., 138:767\u2013779, 2010.","journal-title":"Journ. Stat. Phys."}],"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_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:41:14Z","timestamp":1571791274000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-21810-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030218096","9783030218102"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-21810-2_1","relation":{},"ISSN":["2520-131X","2520-1328"],"issn-type":[{"value":"2520-131X","type":"print"},{"value":"2520-1328","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}