{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T10:22:07Z","timestamp":1780050127411,"version":"3.53.1"},"publisher-location":"New York, NY, USA","edition-number":"1","reference-count":80,"publisher":"ACM","isbn-type":[{"value":"9781450395861","type":"print"}],"license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,2,28]]},"DOI":"10.1145\/3501714.3501752","type":"book-chapter","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T20:58:40Z","timestamp":1646427520000},"page":"671-690","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Causal Models for Dynamical Systems"],"prefix":"10.1145","author":[{"given":"Jonas","family":"Peters","sequence":"first","affiliation":[{"name":"University of Copenhagen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Bauer","sequence":"additional","affiliation":[{"name":"MPI T\u00fcbingen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niklas","family":"Pfister","sequence":"additional","affiliation":[{"name":"University of Copenhagen"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-68560-1"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3929\/ETHZ-B-000385687"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.oep.a041889"},{"key":"e_1_3_2_1_5_1","volume-title":"Nonlinear Parameter Estimation","author":"Bard Y.","unstructured":"Y. Bard . 1974. Nonlinear Parameter Estimation . Academic Press , New York . Y. Bard. 1974. Nonlinear Parameter Estimation. Academic Press, New York."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1510507113"},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the 26th International Conference on Machine Learning (ICML). 2043\u20132051","author":"Bauer S.","unstructured":"S. Bauer , B. Sch\u00f6lkopf , and J. Peters . 2016. The arrow of time in multivariate time series . In Proceedings of the 26th International Conference on Machine Learning (ICML). 2043\u20132051 . S. Bauer, B. Sch\u00f6lkopf, and J. Peters. 2016. The arrow of time in multivariate time series. In Proceedings of the 26th International Conference on Machine Learning (ICML). 2043\u20132051."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3929\/ethz-b-000261734"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-3800(79)90029-2"},{"key":"e_1_3_2_1_10_1","unstructured":"T. Blom and J. M. Mooij. 2018. Generalized structural causal models. ArXiv e-prints (1805.06539).  T. Blom and J. M. Mooij. 2018. Generalized structural causal models. ArXiv e-prints (1805.06539) ."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118619179"},{"key":"e_1_3_2_1_12_1","unstructured":"S. Bongers and J. M. Mooij. 2018. From random differential equations to structural causal models: The stochastic case. ArXiv e-prints (1803.08784).  S. Bongers and J. M. Mooij. 2018. From random differential equations to structural causal models: The stochastic case. ArXiv e-prints (1803.08784) ."},{"key":"e_1_3_2_1_13_1","unstructured":"S. Bongers P. Forr\u00e9 J. Peters and J. M. Mooij. 2016. Foundations of structural causal models with cycles and latent variables. Ann. Stat. ArXiv e-prints (1611.06221v5).  S. Bongers P. Forr\u00e9 J. Peters and J. M. Mooij. 2016. Foundations of structural causal models with cycles and latent variables. Ann. Stat. ArXiv e-prints (1611.06221v5) ."},{"key":"e_1_3_2_1_14_1","unstructured":"B. Calderhead M. Girolami and N. D. Lawrence. 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes. In Advances in Neural Information Processing Systems (NIPS). 217\u2013224.  B. Calderhead M. Girolami and N. D. Lawrence. 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes. In Advances in Neural Information Processing Systems (NIPS) . 217\u2013224."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420010138"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1142\/9789814447300_0004"},{"key":"e_1_3_2_1_17_1","volume-title":"Towards causal inference for spatio-temporal data: conflict and forest loss in colombia. ArXiv e-prints","author":"Christiansen R.","year":"2005","unstructured":"R. Christiansen , M. Baumann , T. Kuemmerle , M. Mahecha , J. Peters . 2020. Towards causal inference for spatio-temporal data: conflict and forest loss in colombia. ArXiv e-prints ( 2005 .08639). R. Christiansen, M. Baumann, T. Kuemmerle, M. Mahecha, J. Peters. 2020. Towards causal inference for spatio-temporal data: conflict and forest loss in colombia. ArXiv e-prints (2005.08639)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-EJS1053"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2007.00634.x"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/17-AOS1631"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1053-8119(03)00202-7"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.2307\/1912791"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.2307\/1906935"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-6265-0"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1214\/EJP.v19-2891"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.ede.0000222409.00878.37"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1017\/S026626710000122X"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2503350"},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 301\u2013310","author":"Hyttinen A.","unstructured":"A. Hyttinen , P. O. Hoyer , F. Eberhardt , and M. J\u00e4rvisalo . 2013. Discovering cyclic causal models with latent variables: A general SAT-based procedure . In Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 301\u2013310 . A. Hyttinen, P. O. Hoyer, F. Eberhardt, and M. J\u00e4rvisalo. 2013. Discovering cyclic causal models with latent variables: A general SAT-based procedure. In Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 301\u2013310."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-985X.2012.01032.x"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139025751"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19991115)18:21<2983::AID-SIM198>3.0.CO;2-A"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1021\/j150111a004"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-27752-1"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2015.00180"},{"key":"e_1_3_2_1_37_1","first-page":"10846","article-title":"Domain adaptation by using causal inference to predict invariant conditional distributions","volume":"31","author":"Magliacane S.","year":"2018","unstructured":"S. Magliacane , T. van Ommen , T. Claassen , S. Bongers , P. Versteeg , and J. M. Mooij . 2018 . Domain adaptation by using causal inference to predict invariant conditional distributions . In Advances in Neural Information Processing Systems 31 , 10846 \u2013 10856 . S. Magliacane, T. van Ommen, T. Claassen, S. Bongers, P. Versteeg, and J. M. Mooij. 2018. Domain adaptation by using causal inference to predict invariant conditional distributions. In Advances in Neural Information Processing Systems 31, 10846\u201310856.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1510493113"},{"key":"e_1_3_2_1_39_1","first-page":"333","article-title":"Die Kinetik der Invertinwirkung","volume":"49","author":"Michaelis L.","year":"1913","unstructured":"L. Michaelis and M. L. Menten . 1913 . Die Kinetik der Invertinwirkung . Biochem Z. 49 , 333 \u2013 369 . Translation available at https:\/\/pubs.acs.org\/doi\/suppl\/10.1021\/bi201284u. L. Michaelis and M. L. Menten. 1913. Die Kinetik der Invertinwirkung. Biochem Z. 49, 333\u2013369. Translation available at https:\/\/pubs.acs.org\/doi\/suppl\/10.1021\/bi201284u.","journal-title":"Biochem Z."},{"key":"e_1_3_2_1_40_1","unstructured":"F. V. Mikkelsen and N. R. Hansen. 2017. Learning large scale ordinary differential equation systems. ArXiv e-prints (1710.09308).  F. V. Mikkelsen and N. R. Hansen. 2017. Learning large scale ordinary differential equation systems. ArXiv e-prints (1710.09308) ."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1214\/19-AOS1821"},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI). 350\u2013360","author":"Mogensen S. W.","unstructured":"S. W. Mogensen , D. Malinsky , and N. R. Hansen . 2018. Causal learning for partially observed stochastic dynamical systems . In Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI). 350\u2013360 . S. W. Mogensen, D. Malinsky, and N. R. Hansen. 2018. Causal learning for partially observed stochastic dynamical systems. In Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI). 350\u2013360."},{"key":"e_1_3_2_1_43_1","volume-title":"Proceedings of the 29th Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI). AUAI Press","author":"Mooij J. M.","unstructured":"J. M. Mooij , D. Janzing , and B. Sch\u00f6lkopf . 2013. From ordinary differential equations to structural causal models: The deterministic case . In Proceedings of the 29th Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI). AUAI Press , Corvallis, Oregon, 440\u2013448. J. M. Mooij, D. Janzing, and B. Sch\u00f6lkopf. 2013. From ordinary differential equations to structural causal models: The deterministic case. In Proceedings of the 29th Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI). AUAI Press, Corvallis, Oregon, 440\u2013448."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781315136370"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu452."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1002\/aic.690440523"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553477"},{"key":"e_1_3_2_1_49_1","unstructured":"J. Peters D. Janzing and B. Sch\u00f6lkopf. 2013. Causal inference on time series using structural equation models. In Advances in Neural Information Processing Systems 26 (NIPS). Curran Associates Inc.  J. Peters D. Janzing and B. Sch\u00f6lkopf. 2013. Causal inference on time series using structural equation models. In Advances in Neural Information Processing Systems 26 (NIPS) . Curran Associates Inc."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.121670"},{"key":"e_1_3_2_1_51_1","unstructured":"J. Peters D. Janzing and B. Sch\u00f6lkopf. 2017. Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press Cambridge MA.  J. Peters D. Janzing and B. Sch\u00f6lkopf. 2017. Elements of Causal Inference: Foundations and Learning Algorithms . MIT Press Cambridge MA."},{"key":"e_1_3_2_1_52_1","unstructured":"N. Pfister S. Bauer and J. Peters. 2018a. Identifying causal structure in large-scale kinetic systems. ArXiv e-prints (1810.11776).  N. Pfister S. Bauer and J. Peters. 2018a. Identifying causal structure in large-scale kinetic systems. ArXiv e-prints (1810.11776) ."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1491403"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1905688116"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2007.00610.x"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv405"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1031689015"},{"key":"e_1_3_2_1_58_1","first-page":"2013","article-title":"Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality. Center for the Statistics and the Social Sciences","volume":"128","author":"Richardson T.","year":"2013","unstructured":"T. Richardson and J. M. Robins . 2013 . Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality. Center for the Statistics and the Social Sciences , University of Washington Series. Working Paper 128 , 30 April 2013 . T. Richardson and J. M. Robins. 2013. Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality. Center for the Statistics and the Social Sciences, University of Washington Series. Working Paper 128, 30 April 2013.","journal-title":"University of Washington Series. Working Paper"},{"key":"e_1_3_2_1_59_1","unstructured":"T. Richardson R. J. Evans J. M. Robins and I. Shpitser. 2017. Nested Markov properties for acyclic directed mixed graphs. ArXiv e-prints (1701.06686).  T. Richardson R. J. Evans J. M. Robins and I. Shpitser. 2017. Nested Markov properties for acyclic directed mixed graphs. ArXiv e-prints (1701.06686) ."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1842-5_4"},{"key":"e_1_3_2_1_61_1","first-page":"1","article-title":"Causal transfer in machine learning","volume":"19","author":"Rojas-Carulla M.","year":"2018","unstructured":"M. Rojas-Carulla , B. Sch\u00f6lkopf , R. Turner , and J. Peters . 2018 . Causal transfer in machine learning . J. Mach. Learn. Res. 19 , 36, 1 \u2013 34 . M. Rojas-Carulla, B. Sch\u00f6lkopf, R. Turner, and J. Peters. 2018. Causal transfer in machine learning. J. Mach. Learn. Res. 19, 36, 1\u201334.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12398"},{"key":"e_1_3_2_1_63_1","volume-title":"Proceedings of the 34th Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI).","author":"Rubenstein P.","unstructured":"P. Rubenstein , S. Bongers , J. M. Mooij , and B. Sch\u00f6lkopf . 2018. From deterministic ODEs to dynamic structural causal models . In Proceedings of the 34th Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI). P. Rubenstein, S. Bongers, J. M. Mooij, and B. Sch\u00f6lkopf. 2018. From deterministic ODEs to dynamic structural causal models. In Proceedings of the 34th Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI)."},{"key":"e_1_3_2_1_64_1","volume-title":"Proceedings of the 29th International Conference on Machine Learning (ICML).","author":"Sch\u00f6lkopf B.","unstructured":"B. Sch\u00f6lkopf , D. Janzing , J. Peters , E. Sgouritsa , K. Zhang , and J. M. Mooij . 2012. On causal and anticausal learning . In Proceedings of the 29th International Conference on Machine Learning (ICML). B. Sch\u00f6lkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. M. Mooij. 2012. On causal and anticausal learning. In Proceedings of the 29th International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.85.461"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.2307\/3211973"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1201\/9780429463976"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248555"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-2748-9"},{"key":"e_1_3_2_1_70_1","volume-title":"Explanation in Causal Inference: Methods for Mediation and Interaction","author":"Vanderweele T. J.","unstructured":"T. J. Vanderweele . 2015. Explanation in Causal Inference: Methods for Mediation and Interaction . Oxford University Press , New York . T. J. Vanderweele. 2015. Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press, New York."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1137\/0903003"},{"key":"e_1_3_2_1_72_1","volume-title":"Proceedings of the 6th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 255\u2013270","author":"Verma T.","unstructured":"T. Verma and J. Pearl . 1991. Equivalence and synthesis of causal models . In Proceedings of the 6th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 255\u2013270 . T. Verma and J. Pearl. 1991. Equivalence and synthesis of causal models. In Proceedings of the 6th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 255\u2013270."},{"key":"e_1_3_2_1_73_1","unstructured":"P. Waage and C. M. Guldberg. 1864. Studier over affiniteten (in Danish). Forhandlinger i Videnskabs-selskabet i Christiania. 35\u201345.  P. Waage and C. M. Guldberg. 1864. Studier over affiniteten (in Danish). Forhandlinger i Videnskabs-selskabet i Christiania . 35\u201345."},{"key":"e_1_3_2_1_74_1","volume-title":"Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS).","author":"Wenk P.","unstructured":"P. Wenk , A. Gotovos , S. Bauer , N. Gorbach , A. Krause , and J. M. Buhmann . 2019. Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs . In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS). P. Wenk, A. Gotovos, S. Bauer, N. Gorbach, A. Krause, and J. M. Buhmann. 2019. Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)."},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbq006"},{"key":"e_1_3_2_1_76_1","volume-title":"Modern Mathematics for Engineers","author":"Wiener N.","unstructured":"N. Wiener . 1956. The theory of prediction . In E. Beckenbach (Ed.), Modern Mathematics for Engineers . McGraw-Hill , New York . N. Wiener. 1956. The theory of prediction. In E. Beckenbach (Ed.), Modern Mathematics for Engineers. McGraw-Hill, New York."},{"key":"e_1_3_2_1_77_1","volume-title":"Stochastic Modelling for Systems Biology","author":"Wilkinson D. J.","unstructured":"D. J. Wilkinson . 2006. Stochastic Modelling for Systems Biology . Chapman and Hall\/CRC Mathematical and Computational Biology Series. Chapman & Hall\/CRC. D. J. Wilkinson. 2006. Stochastic Modelling for Systems Biology. Chapman and Hall\/CRC Mathematical and Computational Biology Series. Chapman & Hall\/CRC."},{"key":"e_1_3_2_1_78_1","first-page":"557","article-title":"Correlation and causation","volume":"20","author":"Wright S.","year":"1921","unstructured":"S. Wright . 1921 . Correlation and causation . J. Agric. Res. 20 , 557 \u2013 585 . S. Wright. 1921. Correlation and causation. J. Agric. Res. 20, 557\u2013585.","journal-title":"J. Agric. Res."},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.3150\/13-BEJSP14"},{"key":"e_1_3_2_1_80_1","unstructured":"B. Yu and K. Kumbier. 2019. Three principles of data science: Predictability computability and stability (pcs). ArXiv e-prints (1901.08152).  B. Yu and K. Kumbier. 2019. Three principles of data science: Predictability computability and stability (pcs). ArXiv e-prints (1901.08152) ."},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1142\/5827"},{"key":"e_1_3_2_1_82_1","volume-title":"Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI). AUAI Press.","author":"Zhang K.","unstructured":"K. Zhang , M. Gong , J. Ramsey , K. Batmanghelich , P. Spirtes , and C. Glymour . 2018. Causal discovery with linear non-Gaussian models under measurement error: Structural identifiability results . In Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI). AUAI Press. K. Zhang, M. Gong, J. Ramsey, K. Batmanghelich, P. Spirtes, and C. Glymour. 2018. Causal discovery with linear non-Gaussian models under measurement error: Structural identifiability results. In Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI). AUAI Press."}],"container-title":["Probabilistic and Causal Inference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3501714.3501752","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3501714.3501752","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:45Z","timestamp":1750191105000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3501714.3501752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"ISBN":["9781450395861"],"references-count":80,"alternative-id":["10.1145\/3501714.3501752","10.1145\/3501714"],"URL":"https:\/\/doi.org\/10.1145\/3501714.3501752","relation":{},"subject":[],"published":{"date-parts":[[2022,2,28]]},"assertion":[{"value":"2022-03-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-03-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}