{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:12:30Z","timestamp":1772554350783,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"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":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467439","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:21:39Z","timestamp":1628878899000},"page":"596-605","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["DARING: Differentiable Causal Discovery with Residual Independence"],"prefix":"10.1145","author":[{"given":"Yue","family":"He","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Cui","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheyan","family":"Shen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renzhe","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Furui","family":"Liu","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1260"},{"key":"e_1_3_2_2_2_1","first-page":"507","article-title":"Optimal structure identification with greedy search","volume":"3","author":"Chickering David Maxwell","year":"2002","unstructured":"David Maxwell Chickering . 2002 . Optimal structure identification with greedy search . Journal of machine learning research , Vol. 3 , Nov (2002), 507 -- 554 . David Maxwell Chickering. 2002. Optimal structure identification with greedy search. Journal of machine learning research, Vol. 3, Nov (2002), 507--554.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/67.3.581"},{"key":"e_1_3_2_2_4_1","volume-title":"MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973","author":"Cao Nicola De","year":"2018","unstructured":"Nicola De Cao and Thomas Kipf . 2018. MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973 ( 2018 ). Nicola De Cao and Thomas Kipf. 2018. MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973 (2018)."},{"key":"e_1_3_2_2_5_1","volume-title":"Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric environment","author":"Gardner Matt W","year":"1998","unstructured":"Matt W Gardner and SR Dorling . 1998. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric environment , Vol. 32 , 14--15 ( 1998 ), 2627--2636. Matt W Gardner and SR Dorling. 1998. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric environment, Vol. 32, 14--15 (1998), 2627--2636."},{"key":"e_1_3_2_2_6_1","volume-title":"Review of causal discovery methods based on graphical models. Frontiers in genetics","author":"Glymour Clark","year":"2019","unstructured":"Clark Glymour , Kun Zhang , and Peter Spirtes . 2019. Review of causal discovery methods based on graphical models. Frontiers in genetics , Vol. 10 ( 2019 ), 524. Clark Glymour, Kun Zhang, and Peter Spirtes. 2019. Review of causal discovery methods based on graphical models. Frontiers in genetics, Vol. 10 (2019), 524."},{"key":"e_1_3_2_2_7_1","first-page":"2672","article-title":"Generative Adversarial Nets","volume":"27","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014 . Generative Adversarial Nets . In Advances in Neural Information Processing Systems , Vol. 27. 2672 -- 2680 . Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems, Vol. 27. 2672--2680.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_8_1","first-page":"1","article-title":"A survey of learning causality with data: Problems and methods","volume":"53","author":"Guo Ruocheng","year":"2020","unstructured":"Ruocheng Guo , Lu Cheng , Jundong Li , P Richard Hahn , and Huan Liu . 2020 . A survey of learning causality with data: Problems and methods . ACM Computing Surveys (CSUR) , Vol. 53 , 4 (2020), 1 -- 37 . Ruocheng Guo, Lu Cheng, Jundong Li, P Richard Hahn, and Huan Liu. 2020. A survey of learning causality with data: Problems and methods. ACM Computing Surveys (CSUR), Vol. 53, 4 (2020), 1--37.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220104"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294834"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220082"},{"key":"e_1_3_2_2_12_1","volume-title":"CASTLE: Regularization via Auxiliary Causal Graph Discovery. arXiv preprint arXiv:2009.13180","author":"Kyono Trent","year":"2020","unstructured":"Trent Kyono , Yao Zhang , and Mihaela van der Schaar . 2020 . CASTLE: Regularization via Auxiliary Causal Graph Discovery. arXiv preprint arXiv:2009.13180 (2020). Trent Kyono, Yao Zhang, and Mihaela van der Schaar. 2020. CASTLE: Regularization via Auxiliary Causal Graph Discovery. arXiv preprint arXiv:2009.13180 (2020)."},{"key":"e_1_3_2_2_13_1","volume-title":"Gradient-Based Neural DAG Learning. In 8th International Conference on Learning Representations, ICLR 2020","author":"Lachapelle S\u00e9","year":"2020","unstructured":"S\u00e9 bastien Lachapelle , Philippe Brouillard , Tristan Deleu , and Simon Lacoste-Julien . 2020 . Gradient-Based Neural DAG Learning. In 8th International Conference on Learning Representations, ICLR 2020 , Addis Ababa, Ethiopia, April 26--30 , 2020. S\u00e9 bastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien. 2020. Gradient-Based Neural DAG Learning. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Yuqing Ma Yue He Fan Ding Sheng Hu Jun Li and Xianglong Liu. 2018. Progressive Generative Hashing for Image Retrieval. In IJCAI. 871--877. Yuqing Ma Yue He Fan Ding Sheng Hu Jun Li and Xianglong Liu. 2018. Progressive Generative Hashing for Image Retrieval. In IJCAI. 871--877.","DOI":"10.24963\/ijcai.2018\/121"},{"key":"e_1_3_2_2_15_1","volume-title":"Interpretable machine learning: definitions, methods, and applications. arXiv preprint arXiv:1901.04592","author":"Murdoch W James","year":"2019","unstructured":"W James Murdoch , Chandan Singh , Karl Kumbier , Reza Abbasi-Asl , and Bin Yu. 2019. Interpretable machine learning: definitions, methods, and applications. arXiv preprint arXiv:1901.04592 ( 2019 ). W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. 2019. Interpretable machine learning: definitions, methods, and applications. arXiv preprint arXiv:1901.04592 (2019)."},{"key":"e_1_3_2_2_16_1","volume-title":"Causality: Models, Reasoning, and Inference.","author":"Neuberg Leland Gerson","year":"2003","unstructured":"Leland Gerson Neuberg . 2003 . Causality: Models, Reasoning, and Inference. Leland Gerson Neuberg. 2003. Causality: Models, Reasoning, and Inference."},{"key":"e_1_3_2_2_17_1","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Ng Ignavier","year":"2020","unstructured":"Ignavier Ng , AmirEmad Ghassami , and Kun Zhang . 2020 . On the Role of Sparsity and DAG Constraints for Learning Linear DAGs . Advances in Neural Information Processing Systems , Vol. 33 (2020). Ignavier Ng, AmirEmad Ghassami, and Kun Zhang. 2020. On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. Advances in Neural Information Processing Systems, Vol. 33 (2020)."},{"key":"e_1_3_2_2_18_1","volume-title":"From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC systems biology","author":"Opgen-Rhein Rainer","year":"2007","unstructured":"Rainer Opgen-Rhein and Korbinian Strimmer . 2007. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC systems biology , Vol. 1 , 1 ( 2007 ), 1--10. Rainer Opgen-Rhein and Korbinian Strimmer. 2007. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC systems biology, Vol. 1, 1 (2007), 1--10."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888910000275"},{"key":"e_1_3_2_2_20_1","first-page":"8026","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , 2019 . PyTorch: An Imperative Style, High-Performance Deep Learning Library . Advances in Neural Information Processing Systems , Vol. 32 (2019), 8026 -- 8037 . Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems, Vol. 32 (2019), 8026--8037.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/ast043"},{"key":"e_1_3_2_2_22_1","volume-title":"Structural intervention distance for evaluating causal graphs. Neural computation","author":"Peters Jonas","year":"2015","unstructured":"Jonas Peters and Peter B\u00fchlmann . 2015. Structural intervention distance for evaluating causal graphs. Neural computation , Vol. 27 , 3 ( 2015 ), 771--799. Jonas Peters and Peter B\u00fchlmann. 2015. Structural intervention distance for evaluating causal graphs. Neural computation, Vol. 27, 3 (2015), 771--799."},{"key":"e_1_3_2_2_23_1","unstructured":"Jonas Peters Joris M Mooij Dominik Janzing and Bernhard Sch\u00f6lkopf. 2014. Causal discovery with continuous additive noise models. (2014). Jonas Peters Joris M Mooij Dominik Janzing and Bernhard Sch\u00f6lkopf. 2014. Causal discovery with continuous additive noise models. (2014)."},{"key":"e_1_3_2_2_24_1","volume-title":"Science","volume":"308","author":"Sachs Karen","year":"2005","unstructured":"Karen Sachs , Omar Perez , Dana Pe'er , Douglas A Lauffenburger , and Garry P Nolan . 2005 . Causal protein-signaling networks derived from multiparameter single-cell data . Science , Vol. 308 , 5721 (2005), 523--529. Karen Sachs, Omar Perez, Dana Pe'er, Douglas A Lauffenburger, and Garry P Nolan. 2005. Causal protein-signaling networks derived from multiparameter single-cell data. Science, Vol. 308, 5721 (2005), 523--529."},{"key":"e_1_3_2_2_25_1","article-title":"A linear non-Gaussian acyclic model for causal discovery","volume":"7","author":"Shimizu Shohei","year":"2006","unstructured":"Shohei Shimizu , Patrik O Hoyer , Aapo Hyv\"arinen, Antti Kerminen , and Michael Jordan . 2006 . A linear non-Gaussian acyclic model for causal discovery . Journal of Machine Learning Research , Vol. 7 , 10 (2006). Shohei Shimizu, Patrik O Hoyer, Aapo Hyv\"arinen, Antti Kerminen, and Michael Jordan. 2006. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, Vol. 7, 10 (2006).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_26_1","volume-title":"prediction, and search","author":"Spirtes Peter","unstructured":"Peter Spirtes , Clark N Glymour , Richard Scheines , and David Heckerman . 2000. Causation , prediction, and search . MIT press . Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. 2000. Causation, prediction, and search .MIT press."},{"key":"e_1_3_2_2_27_1","volume-title":"Causal inference in the presence of latent variables and selection bias. arXiv preprint arXiv:1302.4983","author":"Spirtes Peter L","year":"2013","unstructured":"Peter L Spirtes , Christopher Meek , and Thomas S Richardson . 2013. Causal inference in the presence of latent variables and selection bias. arXiv preprint arXiv:1302.4983 ( 2013 ). Peter L Spirtes, Christopher Meek, and Thomas S Richardson. 2013. Causal inference in the presence of latent variables and selection bias. arXiv preprint arXiv:1302.4983 (2013)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403263"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"e_1_3_2_2_30_1","volume-title":"International Conference on Machine Learning. PMLR, 7154--7163","author":"Yu Yue","year":"2019","unstructured":"Yue Yu , Jie Chen , Tian Gao , and Mo Yu . 2019 . DAG-GNN: DAG structure learning with graph neural networks . In International Conference on Machine Learning. PMLR, 7154--7163 . Yue Yu, Jie Chen, Tian Gao, and Mo Yu. 2019. DAG-GNN: DAG structure learning with graph neural networks. In International Conference on Machine Learning. PMLR, 7154--7163."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2013.03.030"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11555"},{"key":"e_1_3_2_2_33_1","volume-title":"In 25th Conference on Uncertainty in Artificial Intelligence (UAI","author":"Zhang K","year":"2009","unstructured":"K Zhang and A Hyvarinen . 2009 . In 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009). AUAI Press, 647--655. K Zhang and A Hyvarinen. 2009. In 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009). AUAI Press, 647--655."},{"key":"e_1_3_2_2_34_1","first-page":"9472","article-title":"DAGs with NO TEARS: Continuous Optimization for Structure Learning","volume":"31","author":"Zheng Xun","year":"2018","unstructured":"Xun Zheng , Bryon Aragam , Pradeep K Ravikumar , and Eric P Xing . 2018 . DAGs with NO TEARS: Continuous Optimization for Structure Learning . Advances in Neural Information Processing Systems , Vol. 31 (2018), 9472 -- 9483 . Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. 2018. DAGs with NO TEARS: Continuous Optimization for Structure Learning. Advances in Neural Information Processing Systems, Vol. 31 (2018), 9472--9483.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_35_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 3414--3425","author":"Zheng Xun","year":"2020","unstructured":"Xun Zheng , Chen Dan , Bryon Aragam , Pradeep Ravikumar , and Eric Xing . 2020 . Learning sparse nonparametric DAGs . In International Conference on Artificial Intelligence and Statistics. PMLR, 3414--3425 . Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric Xing. 2020. Learning sparse nonparametric DAGs. In International Conference on Artificial Intelligence and Statistics. PMLR, 3414--3425."},{"key":"e_1_3_2_2_36_1","volume-title":"Causal Discovery with Reinforcement Learning. In International Conference on Learning Representations.","author":"Zhu Shengyu","year":"2019","unstructured":"Shengyu Zhu , Ignavier Ng , and Zhitang Chen . 2019 . Causal Discovery with Reinforcement Learning. In International Conference on Learning Representations. Shengyu Zhu, Ignavier Ng, and Zhitang Chen. 2019. Causal Discovery with Reinforcement Learning. In International Conference on Learning Representations."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","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 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467439","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467439","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:37Z","timestamp":1750191517000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467439"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":36,"alternative-id":["10.1145\/3447548.3467439","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467439","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}