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Emergence of scaling in random networks. science , Vol. 286 , 5439 ( 1999 ), 509--512. Albert-L\u00e1szl\u00f3 Barab\u00e1si and R\u00e9ka Albert. 1999. Emergence of scaling in random networks. science, Vol. 286, 5439 (1999), 509--512."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.jors.2600425"},{"key":"e_1_3_2_2_3_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 2314--2322","author":"Bhattacharya Rohit","year":"2021","unstructured":"Rohit Bhattacharya , Tushar Nagarajan , Daniel Malinsky , and Ilya Shpitser . 2021 . Differentiable causal discovery under unmeasured confounding . In International Conference on Artificial Intelligence and Statistics. PMLR, 2314--2322 . Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, and Ilya Shpitser. 2021. Differentiable causal discovery under unmeasured confounding. In International Conference on Artificial Intelligence and Statistics. 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Journal of machine learning research, Vol. 3, Nov (2002), 507--554.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_7_1","volume-title":"Large-sample learning of bayesian networks is np-hard. arXiv preprint arXiv:1212.2468","author":"Chickering David Maxwell","year":"2012","unstructured":"David Maxwell Chickering , Christopher Meek , and David Heckerman . 2012. Large-sample learning of bayesian networks is np-hard. arXiv preprint arXiv:1212.2468 ( 2012 ). David Maxwell Chickering, Christopher Meek, and David Heckerman. 2012. Large-sample learning of bayesian networks is np-hard. arXiv preprint arXiv:1212.2468 (2012)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/1005332.1044703"},{"key":"e_1_3_2_2_9_1","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi John","year":"2011","unstructured":"John Duchi , Elad Hazan , and Yoram Singer . 2011 . Adaptive subgradient methods for online learning and stochastic optimization . Journal of machine learning research , Vol. 12 , 7 (2011). John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, Vol. 12, 7 (2011).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414770"},{"key":"e_1_3_2_2_11_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_12_1","volume-title":"Causal inference in statistics: A primer","author":"Glymour Madelyn","unstructured":"Madelyn Glymour , Judea Pearl , and Nicholas P Jewell . 2016. Causal inference in statistics: A primer . John Wiley & Sons . Madelyn Glymour, Judea Pearl, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer. John Wiley & Sons."},{"key":"e_1_3_2_2_13_1","volume-title":"Explainable and interpretable models in computer vision and machine learning","author":"Goudet Olivier","unstructured":"Olivier Goudet , Diviyan Kalainathan , Philippe Caillou , Isabelle Guyon , David Lopez-Paz , and Michele Sebag . 2018. Learning functional causal models with generative neural networks . In Explainable and interpretable models in computer vision and machine learning . Springer , 39--80. Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michele Sebag. 2018. Learning functional causal models with generative neural networks. In Explainable and interpretable models in computer vision and machine learning. Springer, 39--80."},{"key":"e_1_3_2_2_14_1","first-page":"199","article-title":"On the arzela-ascoli theorem","volume":"34","author":"Green JW","year":"1961","unstructured":"JW Green and FA Valentine . 1961 . On the arzela-ascoli theorem . Mathematics Magazine , Vol. 34 , 4 (1961), 199 -- 202 . JW Green and FA Valentine. 1961. On the arzela-ascoli theorem. Mathematics Magazine, Vol. 34, 4 (1961), 199--202.","journal-title":"Mathematics Magazine"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-017-9195-7"},{"key":"e_1_3_2_2_16_1","volume-title":"Foundations and Trends\u00ae in Optimization","volume":"2","author":"Elad","year":"2016","unstructured":"Elad Hazan et al. 2016. Introduction to online convex optimization . Foundations and Trends\u00ae in Optimization , Vol. 2 , 3--4 ( 2016 ), 157--325. Elad Hazan et al. 2016. Introduction to online convex optimization. Foundations and Trends\u00ae in Optimization, Vol. 2, 3--4 (2016), 157--325."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467439"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455805"},{"key":"e_1_3_2_2_19_1","first-page":"1","article-title":"Causal Discovery from Heterogeneous\/Nonstationary Data","volume":"21","author":"Huang Biwei","year":"2020","unstructured":"Biwei Huang , Kun Zhang , Jiji Zhang , Joseph D Ramsey , Ruben Sanchez-Romero , Clark Glymour , and Bernhard Sch\u00f6lkopf . 2020 b. Causal Discovery from Heterogeneous\/Nonstationary Data . J. Mach. Learn. Res. , Vol. 21 , 89 (2020), 1 -- 53 . Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D Ramsey, Ruben Sanchez-Romero, Clark Glymour, and Bernhard Sch\u00f6lkopf. 2020b. Causal Discovery from Heterogeneous\/Nonstationary Data. J. Mach. Learn. 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Advances in neural information processing systems, Vol. 33 (2020), 9551--9561."},{"key":"e_1_3_2_2_21_1","volume-title":"Unsuitability of NOTEARS for causal graph discovery. arXiv preprint arXiv:2104.05441","author":"Kaiser Marcus","year":"2021","unstructured":"Marcus Kaiser and Maksim Sipos . 2021. Unsuitability of NOTEARS for causal graph discovery. arXiv preprint arXiv:2104.05441 ( 2021 ). Marcus Kaiser and Maksim Sipos. 2021. Unsuitability of NOTEARS for causal graph discovery. arXiv preprint arXiv:2104.05441 (2021)."},{"key":"e_1_3_2_2_22_1","volume-title":"Probabilistic graphical models: principles and techniques","author":"Koller Daphne","unstructured":"Daphne Koller and Nir Friedman . 2009. Probabilistic graphical models: principles and techniques . MIT press . Daphne Koller and Nir Friedman. 2009. Probabilistic graphical models: principles and techniques. MIT press."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1217"},{"key":"e_1_3_2_2_24_1","first-page":"1501","article-title":"CASTLE: regularization via auxiliary causal graph discovery","volume":"33","author":"Kyono Trent","year":"2020","unstructured":"Trent Kyono , Yao Zhang , and Mihaela van der Schaar . 2020 . CASTLE: regularization via auxiliary causal graph discovery . Advances in Neural Information Processing Systems , Vol. 33 (2020), 1501 -- 1512 . Trent Kyono, Yao Zhang, and Mihaela van der Schaar. 2020. CASTLE: regularization via auxiliary causal graph discovery. 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Efficient neural causal discovery without acyclicity constraints. arXiv preprint arXiv:2107.10483 ( 2021 ). Phillip Lippe, Taco Cohen, and Efstratios Gavves. 2021. Efficient neural causal discovery without acyclicity constraints. arXiv preprint arXiv:2107.10483 (2021)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2697063"},{"key":"e_1_3_2_2_28_1","volume-title":"Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine learning","author":"Chickering David Maxwell","year":"1997","unstructured":"David Maxwell Chickering and David Heckerman . 1997. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine learning , Vol. 29 , 2 ( 1997 ), 181--212. David Maxwell Chickering and David Heckerman. 1997. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine learning, Vol. 29, 2 (1997), 181--212."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455815"},{"key":"e_1_3_2_2_30_1","unstructured":"AS Nemirovsky. 1999. Optimization II. Numerical methods for nonlinear continuous optimization. (1999).  AS Nemirovsky. 1999. Optimization II. Numerical methods for nonlinear continuous optimization. (1999)."},{"key":"e_1_3_2_2_31_1","first-page":"17943","article-title":"On the role of sparsity and dag constraints for learning linear dags","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), 17943 -- 17954 . 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), 17943--17954.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_32_1","volume-title":"A graph autoencoder approach to causal structure learning. arXiv preprint arXiv:1911.07420","author":"Ng Ignavier","year":"2019","unstructured":"Ignavier Ng , Shengyu Zhu , Zhitang Chen , and Zhuangyan Fang . 2019. A graph autoencoder approach to causal structure learning. arXiv preprint arXiv:1911.07420 ( 2019 ). Ignavier Ng, Shengyu Zhu, Zhitang Chen, and Zhuangyan Fang. 2019. A graph autoencoder approach to causal structure learning. arXiv preprint arXiv:1911.07420 (2019)."},{"key":"e_1_3_2_2_33_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_34_1","doi-asserted-by":"crossref","unstructured":"Judea Pearl. 1988. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan kaufmann.  Judea Pearl. 1988. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan kaufmann.","DOI":"10.1016\/B978-0-08-051489-5.50008-4"},{"key":"e_1_3_2_2_35_1","volume-title":"Causal inference. Causality: objectives and assessment","author":"Pearl Judea","year":"2010","unstructured":"Judea Pearl . 2010. Causal inference. Causality: objectives and assessment ( 2010 ), 39--58. Judea Pearl. 2010. Causal inference. Causality: objectives and assessment (2010), 39--58."},{"key":"e_1_3_2_2_36_1","volume-title":"Cambridge, UK","author":"Judea Pearl","year":"2000","unstructured":"Judea Pearl et al. 2000 . Models , reasoning and inference. Cambridge, UK : CambridgeUniversityPress , Vol . 19, 2 (2000). Judea Pearl et al. 2000. Models, reasoning and inference. Cambridge, UK: CambridgeUniversityPress, Vol. 19, 2 (2000)."},{"key":"e_1_3_2_2_37_1","volume-title":"Identifiability of gaussian structural equation models with dependent errors having equal variances. arXiv preprint arXiv:1806.08156","author":"Jose M","year":"2018","unstructured":"Jose M Pe na. 2018. Identifiability of gaussian structural equation models with dependent errors having equal variances. arXiv preprint arXiv:1806.08156 ( 2018 ). Jose M Pe na. 2018. Identifiability of gaussian structural equation models with dependent errors having equal variances. arXiv preprint arXiv:1806.08156 (2018)."},{"key":"e_1_3_2_2_38_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_39_1","volume-title":"Elements of causal inference: foundations and learning algorithms","author":"Peters Jonas","unstructured":"Jonas Peters , Dominik Janzing , and Bernhard Sch\u00f6lkopf . 2017. Elements of causal inference: foundations and learning algorithms . The MIT Press . Jonas Peters, Dominik Janzing, and Bernhard Sch\u00f6lkopf. 2017. Elements of causal inference: foundations and learning algorithms. The MIT Press."},{"key":"e_1_3_2_2_40_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_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.elerap.2019.100837"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-016-0032-z"},{"key":"e_1_3_2_2_43_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_44_1","doi-asserted-by":"crossref","unstructured":"Bernhard Sch\u00f6lkopf. 2022. Causality for machine learning. In Probabilistic and Causal Inference: The Works of Judea Pearl. 765--804.  Bernhard Sch\u00f6lkopf. 2022. Causality for machine learning. In Probabilistic and Causal Inference: The Works of Judea Pearl. 765--804.","DOI":"10.1145\/3501714.3501755"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/0022-2496(64)90017-3"},{"key":"e_1_3_2_2_46_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_47_1","volume-title":"A simple approach for finding the globally optimal Bayesian network structure. arXiv preprint arXiv:1206.6875","author":"Silander Tomi","year":"2012","unstructured":"Tomi Silander and Petri Myllymaki . 2012. A simple approach for finding the globally optimal Bayesian network structure. arXiv preprint arXiv:1206.6875 ( 2012 ). Tomi Silander and Petri Myllymaki. 2012. A simple approach for finding the globally optimal Bayesian network structure. arXiv preprint arXiv:1206.6875 (2012)."},{"key":"e_1_3_2_2_48_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_49_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_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273604"},{"key":"e_1_3_2_2_51_1","volume-title":"Learning DAGs without imposing acyclicity. arXiv preprint arXiv:2006.03005","author":"Varando Gherardo","year":"2020","unstructured":"Gherardo Varando . 2020. Learning DAGs without imposing acyclicity. arXiv preprint arXiv:2006.03005 ( 2020 ). Gherardo Varando. 2020. Learning DAGs without imposing acyclicity. arXiv preprint arXiv:2006.03005 (2020)."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3527154"},{"key":"e_1_3_2_2_53_1","first-page":"3895","article-title":"DAGs with No Fears: A closer look at continuous optimization for learning Bayesian networks","volume":"33","author":"Wei Dennis","year":"2020","unstructured":"Dennis Wei , Tian Gao , and Yue Yu . 2020 . DAGs with No Fears: A closer look at continuous optimization for learning Bayesian networks . Advances in Neural Information Processing Systems , Vol. 33 (2020), 3895 -- 3906 . Dennis Wei, Tian Gao, and Yue Yu. 2020. DAGs with No Fears: A closer look at continuous optimization for learning Bayesian networks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 3895--3906.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_54_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_55_1","volume-title":"International Conference on Machine Learning. PMLR, 12156--12166","author":"Yu Yue","year":"2021","unstructured":"Yue Yu , Tian Gao , Naiyu Yin , and Qiang Ji . 2021 . DAGs with no curl: An efficient DAG structure learning approach . In International Conference on Machine Learning. PMLR, 12156--12166 . Yue Yu, Tian Gao, Naiyu Yin, and Qiang Ji. 2021. DAGs with no curl: An efficient DAG structure learning approach. In International Conference on Machine Learning. PMLR, 12156--12166."},{"key":"e_1_3_2_2_56_1","volume-title":"On the identifiability of the post-nonlinear causal model. arXiv preprint arXiv:1205.2599","author":"Zhang Kun","year":"2012","unstructured":"Kun Zhang and Aapo Hyvarinen . 2012. On the identifiability of the post-nonlinear causal model. arXiv preprint arXiv:1205.2599 ( 2012 ). Kun Zhang and Aapo Hyvarinen. 2012. On the identifiability of the post-nonlinear causal model. arXiv preprint arXiv:1205.2599 (2012)."},{"key":"e_1_3_2_2_57_1","volume-title":"Kernel-based conditional independence test and application in causal discovery. arXiv preprint arXiv:1202.3775","author":"Zhang Kun","year":"2012","unstructured":"Kun Zhang , Jonas Peters , Dominik Janzing , and Bernhard Sch\u00f6lkopf . 2012. Kernel-based conditional independence test and application in causal discovery. arXiv preprint arXiv:1202.3775 ( 2012 ). Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Sch\u00f6lkopf. 2012. Kernel-based conditional independence test and application in causal discovery. arXiv preprint arXiv:1202.3775 (2012)."},{"key":"e_1_3_2_2_58_1","volume-title":"Advances in Neural Information Processing Systems","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). 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)."},{"key":"e_1_3_2_2_59_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. 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