{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:17:36Z","timestamp":1726849056230},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T00:00:00Z","timestamp":1482883200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T00:00:00Z","timestamp":1482883200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["1564330"],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2017,3]]},"DOI":"10.1007\/s41060-016-0038-6","type":"journal-article","created":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T18:21:22Z","timestamp":1482949282000},"page":"81-91","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Introduction to the foundations of causal discovery"],"prefix":"10.1007","volume":"3","author":[{"given":"Frederick","family":"Eberhardt","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,12,28]]},"reference":[{"key":"38_CR1","unstructured":"Chalupka, K., Perona, P., Eberhardt, F.: Visual causal feature learning. In: Proceedings of UAI (2015)"},{"key":"38_CR2","unstructured":"Chalupka, K., Perona, P., Eberhardt, F.: Multi-level cause-effect systems. In: Proceedings of AISTATS (2016)"},{"key":"38_CR3","unstructured":"Dash, D.: Restructuring dynamic causal systems in equilibrium. In: Proceedings of AISTATS (2005)"},{"key":"38_CR4","first-page":"192","volume-title":"Lecture Notes in Computer Science","author":"Denver Dash","year":"2001","unstructured":"Dash, D., Druzdzel, M.: Caveats for causal reasoning with equilibrium models. In: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, pp. 192\u2013203. Springer, Berlin (2001)"},{"issue":"5","key":"38_CR5","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1086\/525638","volume":"74","author":"F Eberhardt","year":"2007","unstructured":"Eberhardt, F., Scheines, R.: Interventions and causal inference. Philos. Sci. 74(5), 981\u2013995 (2007)","journal-title":"Philos. Sci."},{"key":"38_CR6","unstructured":"Fisher, R.: The design of experiments. Hafner (1935)"},{"key":"38_CR7","first-page":"333","volume":"17","author":"M Frydenberg","year":"1990","unstructured":"Frydenberg, M.: The chain graph markov property. Scand J Stat 17, 333\u2013353 (1990)","journal-title":"Scand J Stat"},{"key":"38_CR8","unstructured":"Geiger, D., Pearl, J.: On the logic of causal models. In: Proceedings of UAI (1988)"},{"issue":"1","key":"38_CR9","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/S0004-3702(02)00264-3","volume":"141","author":"S Gillispie","year":"2002","unstructured":"Gillispie, S., Perlman, M.: The size distribution for Markov equivalence classes of acyclic digraph models. Artif. Intell. 141(1), 137\u2013155 (2002)","journal-title":"Artif. Intell."},{"key":"38_CR10","volume-title":"Physical Theory and Its Interpretation: Essays in Honor of Jeffrey Bub","author":"C Glymour","year":"2006","unstructured":"Glymour, C.: Markov properties and quantum experiments. In: Demopoulos, W., Pitowsky, I. (eds.) Physical Theory and Its Interpretation: Essays in Honor of Jeffrey Bub. Springer, Berlin (2006)"},{"key":"38_CR11","first-page":"2589","volume":"16","author":"Y He","year":"2015","unstructured":"He, Y., Jia, J., Yu, B.: Counting and exploring sizes of Markov equivalence classes of directed acyclic graphs. J. Mach. Learn. Res. 16, 2589\u20132609 (2015)","journal-title":"J. Mach. Learn. Res."},{"key":"38_CR12","volume-title":"The Routledge Companion to Philosophy of Science","author":"C Hitchcock","year":"2008","unstructured":"Hitchcock, C.: Causation. In: Psillos, S., Curd, M. (eds.) The Routledge Companion to Philosophy of Science. Routledge, London (2008)"},{"key":"38_CR13","unstructured":"Hitchcock, C.: Probabilistic causality. In: Stanford Encyclopedia of Philosophy. The Metaphysics Research Lab, Stanford University, (2010) \n https:\/\/plato.stanford.edu\/cite.html"},{"key":"38_CR14","unstructured":"Hoyer, P., Janzing, D., Mooij, J., Peters, J., Sch\u00f6lkopf, B.: Nonlinear causal discovery with additive noise models. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 21, Curran Associates Inc., pp. 689\u2013696 (2008)"},{"key":"38_CR15","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.ijar.2008.02.006","volume":"49","author":"P Hoyer","year":"2008","unstructured":"Hoyer, P., Shimizu, S., Kerminen, A., Palviainen, M.: Estimation of causal effects using linear non-Gaussian causal models with hidden variables. Int. J. Approx. Reason. 49, 362\u2013378 (2008)","journal-title":"Int. J. Approx. Reason."},{"key":"38_CR16","unstructured":"Hyttinen, A., Eberhardt, F., J\u00e4rvisalo, M.: Constraint-based causal discovery: conflict resolution with answer set programming. In: Proceedings of UAI (2014)"},{"key":"38_CR17","unstructured":"Hyttinen, A., Eberhardt, F., J\u00e4rvisalo, M.: Do-calculus when the true graph is unknown. In: Proceedings of UAI (2015)"},{"key":"38_CR18","unstructured":"Hyttinen, A., Hoyer, P., Eberhardt, F., J\u00e4rvisalo, M.: Discovering cyclic causal models with latent variables: a general SAT-based procedure. In: Proceedings of UAI, pp. 301\u2013310. AUAI Press (2013)"},{"key":"38_CR19","unstructured":"Hyttinen, A., Plis, S., J\u00e4rvisalo, M., Eberhardt, F., Danks, D.: Causal discovery from subsampled time series data by constraint optimization. In: Proceedings of PGM (2016)"},{"key":"38_CR20","volume-title":"Independent Component Analysis","author":"A Hyv\u00e4rinen","year":"2004","unstructured":"Hyv\u00e4rinen, A., Karhunen, J., Oja, E.: Independent Component Analysis, vol. 46. Wiley, London (2004)"},{"issue":"11","key":"38_CR21","doi-asserted-by":"publisher","first-page":"3617","DOI":"10.1007\/s11229-014-0637-5","volume":"192","author":"B Jantzen","year":"2015","unstructured":"Jantzen, B.: Projection, symmetry, and natural kinds. Synthese 192(11), 3617\u20133646 (2015)","journal-title":"Synthese"},{"issue":"2","key":"38_CR22","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1038\/scientificamerican0203-74","volume":"288","author":"A Klatsky","year":"2003","unstructured":"Klatsky, A.: Drink to your health? Scientific American 288(2), 75\u201381 (2003)","journal-title":"Scientific American"},{"key":"38_CR23","unstructured":"Lacerda, G., Spirtes, P., Ramsey, J., Hoyer, P.O.: Discovering cyclic causal models by independent components analysis. In: Proceedings of UAI, pp. 366\u2013374 (2008)"},{"key":"38_CR24","unstructured":"Magliacane, S., Claassen, T., Mooij, J.: Ancestral causal inference. \n arXiv:1606.07035\n \n (2016)"},{"key":"38_CR25","unstructured":"Maier, M., Marazopoulou, K., Arbour, D., Jensen, D.: A sound and complete algorithm for learning causal models from relational data. Proceedings of UAI (2013)"},{"key":"38_CR26","unstructured":"Meek, C.: Strong completeness and faithfulness in Bayesian networks. In: Proceedings of UAI, pp. 411\u2013418. Morgan Kaufmann Publishers Inc. (1995)"},{"key":"38_CR27","doi-asserted-by":"crossref","unstructured":"Mooij, J., Janzing, D., Peters, J., Sch\u00f6lkopf, B.: Regression by dependence minimization and its application to causal inference in additive noise models. In: Proceedings of ICML, pp. 745\u2013752 (2009)","DOI":"10.1145\/1553374.1553470"},{"key":"38_CR28","unstructured":"Nyberg, E., Korb, K.: Informative interventions. In: Williamson, J., (ed.) Causality and Probability in the Sciences. College Publications (2006)"},{"key":"38_CR29","unstructured":"Park, G., Raskutti, G.: Learning large-scale poisson dag models based on overdispersion scoring. In: Advances in Neural Information Processing Systems, pp. 631\u2013639 (2015)"},{"key":"38_CR30","volume-title":"Probabilistic Reasoning in Intelligent Systems","author":"J Pearl","year":"1988","unstructured":"Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, Los Altos (1988)"},{"key":"38_CR31","volume-title":"Causality","author":"J Pearl","year":"2000","unstructured":"Pearl, J.: Causality. Oxford University Press, Oxford (2000)"},{"key":"38_CR32","unstructured":"Pearl, J., Verma, T.: Equivalence and synthesis of causal models. In: Proceedings of Sixth Conference on Uncertainty in Artijicial Intelligence, pp. 220\u2013227 (1991)"},{"key":"38_CR33","unstructured":"Peters, J., Janzing, D., Sch\u00f6lkopf, B.: Identifying cause and effect on discrete data using additive noise models. In: Proceedings of AISTATS, pp. 597\u2013604 (2010)"},{"issue":"12","key":"38_CR34","doi-asserted-by":"publisher","first-page":"2436","DOI":"10.1109\/TPAMI.2011.71","volume":"33","author":"J Peters","year":"2011","unstructured":"Peters, J., Janzing, D., Sch\u00f6lkopf, B.: Causal inference on discrete data using additive noise models. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2436\u20132450 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR35","unstructured":"Peters, J., Mooij, J., Janzing, D., Sch\u00f6lkopf, B.: Identifiability of causal graphs using functional models. In: Proceedings of UAI, pp. 589\u2013598. AUAI Press (2011)"},{"key":"38_CR36","unstructured":"Richardson, T.: Feedback models: Interpretation and discovery. Ph.D. thesis, Carnegie Mellon University (1996)"},{"issue":"1","key":"38_CR37","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s10994-013-5362-7","volume":"94","author":"O Schulte","year":"2014","unstructured":"Schulte, O., Khosravi, H., Kirkpatrick, A., Gao, T., Zhu, Y.: Modelling relational statistics with Bayes nets. Mach. Learn. 94(1), 105\u2013125 (2014)","journal-title":"Mach. Learn."},{"key":"38_CR38","unstructured":"Shalizi, C., Moore, C.: What is a macrostate? Subjective observations and objective dynamics. \n arXiv:cond-mat\/0303625\n \n (2003)"},{"key":"38_CR39","first-page":"2003","volume":"7","author":"S Shimizu","year":"2006","unstructured":"Shimizu, S., Hoyer, P., Hyv\u00e4rinen, A., Kerminen, A.: A linear non-Gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7, 2003\u20132030 (2006)","journal-title":"J. Mach. Learn. Res."},{"issue":"100","key":"38_CR40","first-page":"1","volume":"19","author":"A Sokol","year":"2014","unstructured":"Sokol, A., Hansen, N.: Causal interpretation of stochastic differential equations. Electron. J. Probab. 19(100), 1\u201324 (2014)","journal-title":"Electron. J. Probab."},{"key":"38_CR41","volume-title":"Causation, Prediction and Search","author":"P Spirtes","year":"2000","unstructured":"Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search, 2nd edn. MIT Press, Cambridge (2000)","edition":"2"},{"key":"38_CR42","doi-asserted-by":"publisher","unstructured":"Spirtes, P., Zhang, K.: Causal discovery and inference: concepts and recent methodological advances. Appl. Inform. 3, 3 (2016). doi:\n 10.1186\/s40535-016-0018-x","DOI":"10.1186\/s40535-016-0018-x"},{"issue":"21","key":"38_CR43","doi-asserted-by":"publisher","first-page":"2819","DOI":"10.1093\/bioinformatics\/bts523","volume":"28","author":"D Stekhoven","year":"2012","unstructured":"Stekhoven, D., Moraes, I., Sveinbj\u00f6rnsson, G., Hennig, L., Maathuis, M., B\u00fchlmann, P.: Causal stability ranking. Bioinformatics 28(21), 2819\u20132823 (2012)","journal-title":"Bioinformatics"},{"issue":"1","key":"38_CR44","doi-asserted-by":"publisher","first-page":"41","DOI":"10.2333\/bhmk.41.41","volume":"41","author":"R Tillman","year":"2014","unstructured":"Tillman, R., Eberhardt, F.: Learning causal structure from multiple datasets with similar variable sets. Behaviormetrika 41(1), 41\u201364 (2014)","journal-title":"Behaviormetrika"},{"key":"38_CR45","first-page":"2147","volume":"16","author":"S Triantafillou","year":"2015","unstructured":"Triantafillou, S., Tsamardinos, I.: Constraint-based causal discovery from multiple interventions over overlapping variable sets. J. Mach. Learn. Res. 16, 2147\u20132205 (2015)","journal-title":"J. Mach. Learn. Res."},{"key":"38_CR46","unstructured":"Triantafillou, S., Tsamardinos, I., Tollis, I.G.: Learning causal structure from overlapping variable sets. In: Proceedings of AISTATS, pp. 860\u2013867. JMLR (2010)"},{"issue":"2","key":"38_CR47","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1214\/12-AOS1080","volume":"41","author":"C Uhler","year":"2013","unstructured":"Uhler, C., Raskutti, G., B\u00fchlmann, P., Yu, B.: Geometry of the faithfulness assumption in causal inference. Ann. Stat. 41(2), 436\u2013463 (2013)","journal-title":"Ann. Stat."},{"key":"38_CR48","unstructured":"Voortman, M., Dash, D., Druzdzel, M.: Learning why things change: the difference-based causality learner. arXiv preprint \n arXiv:1203.3525\n \n (2012)"},{"issue":"4","key":"38_CR49","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1007\/s11229-015-0673-9","volume":"193","author":"J Zhang","year":"2016","unstructured":"Zhang, J., Spirtes, P.: The three faces of faithfulness. Synthese 193(4), 1011\u20131027 (2016)","journal-title":"Synthese"},{"key":"38_CR50","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/11893295_45","volume-title":"Neural Information Processing","author":"Kun Zhang","year":"2006","unstructured":"Zhang, K., Chan, L.W.: Extensions of ICA for causality discovery in the Hong Kong stock market. In: International Conference on Neural Information Processing, pp. 400\u2013409. Springer, Berlin (2006)"},{"key":"38_CR51","unstructured":"Zhang, K., Hyv\u00e4rinen, A.: On the identifiability of the post-nonlinear causal model. In: Proceedings of UAI, pp. 647\u2013655. AUAI Press (2009)"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s41060-016-0038-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-016-0038-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-016-0038-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,17]],"date-time":"2020-05-17T00:29:13Z","timestamp":1589675353000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s41060-016-0038-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,28]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,3]]}},"alternative-id":["38"],"URL":"https:\/\/doi.org\/10.1007\/s41060-016-0038-6","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,28]]},"assertion":[{"value":"31 October 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2016","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2016","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}