{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:22:35Z","timestamp":1776093755923,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62002343 62077044"],"award-info":[{"award-number":["62002343 62077044"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599552","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:13:58Z","timestamp":1691172838000},"page":"5803-5804","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Causal Discovery from Temporal Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7941-5011","authenticated-orcid":false,"given":"Chang","family":"Gong","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1778-8319","authenticated-orcid":false,"given":"Di","family":"Yao","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1510-1835","authenticated-orcid":false,"given":"Chuzhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3258-1116","authenticated-orcid":false,"given":"Wenbin","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7858-4980","authenticated-orcid":false,"given":"Jingping","family":"Bi","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7625-0650","authenticated-orcid":false,"given":"Lun","family":"Du","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3172-6133","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"additional","affiliation":[{"name":"Megagon Labs, Mountain View, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2017.00068"},{"key":"e_1_3_2_1_2_1","unstructured":"Debarun Bhattacharjya Karthikeyan Shanmugam Tian Gao and D. Subramanian. 2022. Process Independence Testing in Proximal Graphical Event Models. In CLeaR.  Debarun Bhattacharjya Karthikeyan Shanmugam Tian Gao and D. Subramanian. 2022. Process Independence Testing in Proximal Graphical Event Models. In CLeaR."},{"key":"e_1_3_2_1_3_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Bhattacharjya Debarun","year":"2018","unstructured":"Debarun Bhattacharjya , Dharmashankar Subramanian , and Tian Gao . 2018 . Proximal Graphical Event Models . In Advances in Neural Information Processing Systems , Vol. 31 . Curran Associates, Inc. Debarun Bhattacharjya, Dharmashankar Subramanian, and Tian Gao. 2018. Proximal Graphical Event Models. In Advances in Neural Information Processing Systems, Vol. 31. Curran Associates, Inc."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3406460"},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning, ICML 2022","volume":"162","author":"Gao Tian","year":"2022","unstructured":"Tian Gao , Debarun Bhattacharjya , Elliot Nelson , Miao Liu , and Yue Yu . 2022 . IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data . In International Conference on Machine Learning, ICML 2022 , 17--23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research) , Vol. 162 . PMLR, 6988--7001. Tian Gao, Debarun Bhattacharjya, Elliot Nelson, Miao Liu, and Yue Yu. 2022. IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data. In International Conference on Machine Learning, ICML 2022, 17--23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research), Vol. 162. PMLR, 6988--7001."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Chang Gong Di Yao Chuzhe Zhang Wenbin Li and Jingping Bi. 2023. Causal Discovery from Temporal Data: An Overview and New Perspectives. arxiv: 2303.10112 [cs.LG]  Chang Gong Di Yao Chuzhe Zhang Wenbin Li and Jingping Bi. 2023. Causal Discovery from Temporal Data: An Overview and New Perspectives. arxiv: 2303.10112 [cs.LG]","DOI":"10.1145\/3580305.3599552"},{"key":"e_1_3_2_1_7_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Ide Tsuyoshi","year":"2021","unstructured":"Tsuyoshi Ide , Georgios Kollias , Dzung Phan , and Naoki Abe . 2021 . Cardinality-Regularized Hawkes-Granger Model . In Advances in Neural Information Processing Systems , Vol. 34 . Curran Associates, Inc., 2682--2694. Tsuyoshi Ide, Georgios Kollias, Dzung Phan, and Naoki Abe. 2021. Cardinality-Regularized Hawkes-Granger Model. In Advances in Neural Information Processing Systems, Vol. 34. Curran Associates, Inc., 2682--2694."},{"key":"e_1_3_2_1_8_1","volume-title":"IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence","author":"Kleinberg Samantha","year":"2011","unstructured":"Samantha Kleinberg . 2011 . A Logic for Causal Inference in Time Series with Discrete and Continuous Variables . In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence , Barcelona, Catalonia, Spain, July 16--22 , 2011. IJCAI\/AAAI, 943--950. Samantha Kleinberg. 2011. A Logic for Causal Inference in Time Series with Discrete and Continuous Variables. In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16--22, 2011. IJCAI\/AAAI, 943--950."},{"key":"e_1_3_2_1_9_1","volume-title":"Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Li Yunzhu","year":"2020","unstructured":"Yunzhu Li , Antonio Torralba , Anima Anandkumar , Dieter Fox , and Animesh Garg . 2020 . Causal Discovery in Physical Systems from Videos . In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 , NeurIPS 2020, December 6--12, 2020, virtual. Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, and Animesh Garg. 2020. Causal Discovery in Physical Systems from Videos. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Machine Learning, ICML 2022","volume":"162","author":"Lippe Phillip","year":"2022","unstructured":"Phillip Lippe , Sara Magliacane , Sindy L\u00f6 we, Yuki M. Asano , Taco Cohen , and Stratis Gavves . 2022 . CITRIS: Causal Identifiability from Temporal Intervened Sequences . In International Conference on Machine Learning, ICML 2022 , 17--23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research) , Vol. 162 . PMLR, 13557--13603. Phillip Lippe, Sara Magliacane, Sindy L\u00f6 we, Yuki M. Asano, Taco Cohen, and Stratis Gavves. 2022. CITRIS: Causal Identifiability from Temporal Intervened Sequences. In International Conference on Machine Learning, ICML 2022, 17--23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research), Vol. 162. PMLR, 13557--13603."},{"key":"e_1_3_2_1_11_1","volume-title":"1st Conference on Causal Learning and Reasoning, CLeaR 2022, Sequoia Conference Center","volume":"177","author":"Sindy L\u00f6","year":"2022","unstructured":"Sindy L\u00f6 we, David Madras , Richard Z. Shilling , and Max Welling . 2022 . Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data . In 1st Conference on Causal Learning and Reasoning, CLeaR 2022, Sequoia Conference Center , Eureka, CA, USA, 11- -13 April, 2022 (Proceedings of Machine Learning Research), Vol. 177 . PMLR, 509--525. Sindy L\u00f6 we, David Madras, Richard Z. Shilling, and Max Welling. 2022. Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. In 1st Conference on Causal Learning and Reasoning, CLeaR 2022, Sequoia Conference Center, Eureka, CA, USA, 11--13 April, 2022 (Proceedings of Machine Learning Research), Vol. 177. PMLR, 509--525."},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2018","author":"Malinsky Daniel","year":"2018","unstructured":"Daniel Malinsky and Peter Spirtes . 2018 . Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding . In Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2018 , London, UK , 20 August 2018 (Proceedings of Machine Learning Research), Vol. 92. PMLR, 23--47. Daniel Malinsky and Peter Spirtes. 2018. Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. In Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2018, London, UK, 20 August 2018 (Proceedings of Machine Learning Research), Vol. 92. PMLR, 23--47."},{"key":"e_1_3_2_1_13_1","volume-title":"Interpretable Models for Granger Causality Using Self-explaining Neural Networks. In 9th International Conference on Learning Representations, ICLR 2021","author":"Marcinkevics Ricards","year":"2021","unstructured":"Ricards Marcinkevics and Julia E. Vogt . 2021 . Interpretable Models for Granger Causality Using Self-explaining Neural Networks. In 9th International Conference on Learning Representations, ICLR 2021 , Virtual Event, Austria, May 3--7 , 2021 . OpenReview.net. Ricards Marcinkevics and Julia E. Vogt. 2021. Interpretable Models for Granger Causality Using Self-explaining Neural Networks. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/IWQoS49365.2020.9213058"},{"key":"e_1_3_2_1_15_1","volume-title":"The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26--28","volume":"108","author":"Pamfil Roxana","year":"2020","unstructured":"Roxana Pamfil , Nisara Sriwattanaworachai , Shaan Desai , Philip Pilgerstorfer , Konstantinos Georgatzis , Paul Beaumont , and Bryon Aragam . 2020 . DYNOTEARS: Structure Learning from Time-Series Data . In The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26--28 August 2020, Online [Palermo, Sicily, Italy] (Proceedings of Machine Learning Research) , Vol. 108 . PMLR, 1595--1605. Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, and Bryon Aragam. 2020. DYNOTEARS: Structure Learning from Time-Series Data. In The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26--28 August 2020, Online [Palermo, Sicily, Italy] (Proceedings of Machine Learning Research), Vol. 108. PMLR, 1595--1605."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020","author":"Runge Jakob","year":"2020","unstructured":"Jakob Runge . 2020 . Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets . In Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020 , virtual online, August 3 --6 , 2020 (Proceedings of Machine Learning Research), Vol. 124. AUAI Press, 1388--1397. Jakob Runge. 2020. Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. In Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3--6, 2020 (Proceedings of Machine Learning Research), Vol. 124. AUAI Press, 1388--1397."},{"key":"e_1_3_2_1_17_1","volume-title":"Detecting causality in complex ecosystems. science","author":"Sugihara George","year":"2012","unstructured":"George Sugihara , Robert May , Hao Ye , Chih-hao Hsieh, Ethan Deyle , Michael Fogarty , and Stephan Munch . 2012. Detecting causality in complex ecosystems. science , Vol. 338 , 6106 ( 2012 ), 496--500. George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, and Stephan Munch. 2012. Detecting causality in complex ecosystems. science, Vol. 338, 6106 (2012), 496--500."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3470792"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3065601"},{"key":"e_1_3_2_1_20_1","volume-title":"Druzdzel","author":"Voortman Mark","year":"2010","unstructured":"Mark Voortman , Denver Dash , and Marek J . Druzdzel . 2010 . Learning Why Things Change: The Difference-Based Causality Learner. In UAI 2010, Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, July 8--11, 2010. AUAI Press , 641--650. Mark Voortman, Denver Dash, and Marek J. Druzdzel. 2010. Learning Why Things Change: The Difference-Based Causality Learner. In UAI 2010, Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, July 8--11, 2010. AUAI Press, 641--650."},{"key":"e_1_3_2_1_21_1","volume-title":"The Tenth International Conference on Learning Representations, ICLR 2022","author":"Wu Alexander P.","year":"2022","unstructured":"Alexander P. Wu , Rohit Singh , and Bonnie Berger . 2022 a. Granger causal inference on DAGs identifies genomic loci regulating transcription . In The Tenth International Conference on Learning Representations, ICLR 2022 , Virtual Event, April 25--29 , 2022. Alexander P. Wu, Rohit Singh, and Bonnie Berger. 2022a. Granger causal inference on DAGs identifies genomic loci regulating transcription. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557660"},{"key":"e_1_3_2_1_23_1","volume-title":"CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Yao Di","year":"2022","unstructured":"Di Yao , Chang Gong , Lei Zhang , Sheng Chen , and Jingping Bi . 2022 . CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , Washington, DC, USA, August 14 - 18 , 2022. ACM, 4342--4352. Di Yao, Chang Gong, Lei Zhang, Sheng Chen, and Jingping Bi. 2022. CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. ACM, 4342--4352."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research)","volume":"119","author":"Zhang Wei","year":"2020","unstructured":"Wei Zhang , Thomas Panum , Somesh Jha , Prasad Chalasani , and David Page . 2020 . CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods . In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Vol. 119 . PMLR, 11235--11245. Wei Zhang, Thomas Panum, Somesh Jha, Prasad Chalasani, and David Page. 2020. CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research), Vol. 119. PMLR, 11235--11245."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3470795"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599552","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599552","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:52Z","timestamp":1750178272000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":25,"alternative-id":["10.1145\/3580305.3599552","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599552","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}