{"type":"ScholarlyArticle","title":"Scalable Probabilistic Causal Structure Discovery","authors":[{"name":"Dhanya Sridhar","slug":"dhanya-sridhar","given":"Dhanya","family":"Sridhar","affiliation":"University of California Santa Cruz"},{"name":"Jay Pujara","slug":"jay-pujara","given":"Jay","family":"Pujara","affiliation":"University of Southern California"},{"name":"Lise Getoor","slug":"lise-getoor","given":"Lise","family":"Getoor","affiliation":"University of California Santa Cruz"}],"abstract":"Complex causal networks underlie many real-world problems, from the regulatory interactions between genes to the environmental patterns used to understand climate change. Computational methods seek to infer these causal networks using observational data and domain knowledge. In this paper, we identify three key requirements for inferring the structure of causal networks for scientific discovery: (1) robustness to noise in observed measurements; (2) scalability to handle hundreds of variables; and (3) flexibility to encode domain knowledge and other structural constraints. We first formalize the problem of joint probabilistic causal structure discovery.  We develop an approach using probabilistic soft logic (PSL) that exploits multiple statistical tests, supports efficient optimization over hundreds of variables, and can easily incorporate structural constraints, including imperfect domain knowledge. We compare our method against multiple well-studied approaches on biological and synthetic datasets, showing improvements of up to 20% in F1-score over the best performing baseline in realistic settings.","doi":"10.24963/ijcai.2018/709","url":"https://ijcai.org/proceedings/2018/709","pdf":"https://ijcai.org/proceedings/2018/0709.pdf","markdown":"https://ijcai.org/proceedings/2018/709.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/709","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Uncertainty in AI","track":"Main track","number":709,"pages":{"first":5112,"last":5118},"keywords":["Uncertainty in AI: Bayesian Networks","Knowledge Representation and Reasoning: Action, Change and Causality","Machine Learning: Learning Graphical Models","Constraints and SAT: Constraints and Data Mining ; Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Bach et al., 2017] Stephen H Bach, Matthias Broecheler, Bert Huang, and Lise Getoor. Hinge-loss Markov ran- dom fields and probabilistic soft logic. Journal of Machine Learning Research, 18(109):1-67, 2017.","title":"Hinge-loss Markov random fields and probabilistic soft logic","authors":"Matthias Stephen H Bach, Bert Broecheler, Lise Huang, Getoor","year":"2017","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bartlett and Cussens, 2017] Mark Bartlett and James Cussens. Integer linear programming for the Bayesian network structure learning problem. Artificial Intelligence, 244:258-271, 2017.","title":"Integer linear programming for the Bayesian network structure learning problem","authors":"Mark Bartlett, James Cussens","year":"2017","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chickering, 2003] David Maxwell Chickering. Optimal structure identification with greedy search. Journal of Ma- chine Learning Research, 3:507-554, 2003.","title":"Optimal structure identification with greedy search","authors":"David Maxwell, Chickering","year":"2003","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Claassen and Heskes, 2011] Tom Claassen and Tom Hes- kes. A logical characterization of constraint-based causal discovery. In UAI, pages 135-144, 2011.","title":"Tom Claassen and Tom Heskes. A logical characterization of constraint-based causal discovery","authors":"Heskes Claassen","year":"2011","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Colombo and Maathuis, 2014] Diego Colombo and Mar- loes H Maathuis. Order-independent constraint-based causal structure learning. Journal of Machine Learning Research, 15:3741-3782, 2014.","title":"Diego Colombo and Marloes H Maathuis. Order-independent constraint-based causal structure learning","authors":"Maathuis Colombo","year":"2014","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cussens, 2011] James Cussens. Bayesian network learning with cutting planes. In UAI, pages 153-160, 2011.","title":"Bayesian network learning with cutting planes","authors":"James Cussens","year":"2011","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[De Campos and Ji, 2011] Cassio P De Campos and Qiang Ji. Efficient structure learning of Bayesian networks us- ing constraints. Journal of Machine Learning Research, 12:663-689, 2011.","title":"Efficient structure learning of Bayesian networks using constraints","authors":"Cassio, De Campos, Qiang Ji","year":"2011","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Eaton and Murphy, 2007] Daniel Eaton and Kevin P Mur- phy. Exact Bayesian structure learning from uncertain in- terventions. In AISTATS, pages 107-114, 2007.","title":"Exact Bayesian structure learning from uncertain interventions","authors":"Daniel Eaton, Kevin P Murphy","year":"2007","venue":"AISTATS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ebert-Uphoff and Deng, 2012] Imme Ebert-Uphoff and Yi Deng. Causal discovery for climate research using graphical models. Journal of Climate, 25(17):5648-5665, 2012.","title":"Causal discovery for climate research using graphical models","authors":"Deng ; Imme Ebert-Uphoff, Yi Ebert-Uphoff, Deng","year":"2012","venue":"Journal of Climate","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Friedman, 2004] Nir Friedman. Inferring cellular net- works using probabilistic graphical models. Science, 303(5659):799-805, 2004.","title":"Inferring cellular networks using probabilistic graphical models","authors":"Nir Friedman","year":"2004","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hyttinen et al., 2013] Antti Hyttinen, Patrik O Hoyer, Fred- erick Eberhardt, and Matti Jarvisalo. Discovering cyclic causal models with latent variables: A general sat-based procedure. In UAI, pages 301-310, 2013.","title":"Discovering cyclic causal models with latent variables: A general sat-based procedure","authors":"Antti Hyttinen, Patrik O Hoyer, Frederick Eberhardt, Matti Jarvisalo","year":"2013","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hyttinen et al., 2014] Antti Hyttinen, Frederick Eberhardt, and Matti Järvisalo. Constraint-based causal discovery: Conflict resolution with answer set programming. In UAI, pages 340-349, 2014.","title":"Constraint-based causal discovery: Conflict resolution with answer set programming","authors":"Hyttinen","year":"2014","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Jaakkola et al., 2010] Tommi Jaakkola, David Sontag, Amir Globerson, and Marina Meila. Learning Bayesian network structure using LP relaxations. In AISTATS, pages 358- 365, 2010.","title":"Learning Bayesian network structure using LP relaxations","authors":"Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila","year":"2010","venue":"AISTATS","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2016] Fei Liu, Shao-Wu Zhang, Wei-Feng Guo, Ze-Gang Wei, and Luonan Chen. Inference of gene reg- ulatory network based on local Bayesian networks. PLoS computational biology, 12(8):e1005024, 2016.","title":"Inference of gene regulatory network based on local Bayesian networks","authors":"Fei Liu, Shao-Wu Zhang, Wei-Feng Guo, Ze-Gang Wei, Luonan Chen","year":"2016","venue":"PLoS computational biology","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Magliacane et al., 2016] Sara Magliacane, Tom Claassen, and Joris M Mooij. Ancestral causal inference. In NIPS, pages 4466-4474, 2016.","title":"Ancestral causal inference","authors":"Sara Magliacane, Tom Claassen, Joris M Mooij","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Marbach et al., 2010] Daniel Marbach, Robert J Prill, Thomas Schaffter, Claudio Mattiussi, Dario Floreano, and Gustavo Stolovitzky. Revealing strengths and weaknesses of methods for gene network inference. Proceedings of the national academy of sciences, 107:6286-6291, 2010.","title":"Revealing strengths and weaknesses of methods for gene network inference","authors":"Daniel Marbach, Robert J Prill, Thomas Schaffter, Claudio Mattiussi, Dario Floreano, Gustavo Stolovitzky","year":"2010","venue":"Proceedings of the national academy of sciences","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mooij and Heskes, 2013] Joris Mooij and Tom Heskes. Cyclic causal discovery from continuous equilibrium data. In UAI, pages 431-439, 2013.","title":"Cyclic causal discovery from continuous equilibrium data","authors":"Heskes Mooij, Joris Mooij, Tom Heskes","year":"2013","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Peters et al., 2016] Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen. Causal inference by using invariant prediction: identification and confidence intervals. Jour- nal of the Royal Statistical Society, 78:947-1012, 2016.","title":"Causal inference by using invariant prediction: identification and confidence intervals","authors":"Peters","year":"2016","venue":"Journal of the Royal Statistical Society","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Prill et al., 2010] Robert J Prill, Daniel Marbach, Julio Saez-Rodriguez, Peter K Sorger, Leonidas G Alexopoulos, Xiaowei Xue, Neil D Clarke, Gregoire Altan-Bonnet, and Gustavo Stolovitzky. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PloS one, 5:e9202, 2010.","title":"Towards a rigorous assessment of systems biology models: the DREAM3 challenges","authors":"Daniel Robert J Prill, Julio Marbach, Peter K Saez-Rodriguez, Leonidas G Sorger, Xiaowei Alexopoulos, Xue, Gregoire Neil D Clarke, Gustavo Altan-Bonnet, Stolovitzky","year":"2010","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ramsey et al., 2006] Joseph Ramsey, Jiji Zhang, and Pe- ter L Spirtes. Adjacency-faithfulness and conservative causal inference. In UAI, pages 401-408, 2006.","title":"Adjacency-faithfulness and conservative causal inference","authors":"Joseph Ramsey, Jiji Zhang, Peter L Spirtes","year":"2006","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Ramsey, 2010] Joseph Ramsey. Bootstrapping the PC and CPC algorithms to improve search accuracy. 2010.","title":"Bootstrapping the PC and CPC algorithms to improve search accuracy","authors":"Joseph Ramsey","year":"2010","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Sachs et al., 2005] Karen Sachs, Omar Perez, Dana Pe'er, Douglas A Lauffenburger, and Garry P Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308:523-529, 2005.","title":"Causal protein-signaling networks derived from multiparameter single-cell data","authors":"Karen Sachs, Omar Perez, Dana Pe'er, Douglas A Lauffenburger, Garry P Nolan","year":"2005","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Spirtes and Glymour, 1991] Peter Spirtes and Clark Gly- mour. An algorithm for fast recovery of sparse causal graphs. Social science computer review, 9:62-72, 1991.","title":"Peter Spirtes and Clark Glymour. An algorithm for fast recovery of sparse causal graphs","authors":"Glymour Spirtes","year":"1991","venue":"Social science computer review","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Triantafillou and Tsamardinos, 2015] Sofia Triantafillou and Ioannis Tsamardinos. Constraint-based causal discovery from multiple interventions over overlapping variable sets. Journal of Machine Learning Research, 16:2147-2205, 2015.","title":"Constraint-based causal discovery from multiple interventions over overlapping variable sets","authors":"Tsamardinos Triantafillou, Sofia Triantafillou, Ioannis Tsamardinos","year":"2015","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tsamardinos et al., 2006] Ioannis Tsamardinos, Laura E Brown, and Constantin F Aliferis. The max-min hill- climbing Bayesian network structure learning algorithm. Machine Learning, 65:31-78, 2006.","title":"The max-min hillclimbing Bayesian network structure learning algorithm","authors":"Ioannis Tsamardinos, Laura E Brown, Constantin F Aliferis","year":"2006","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Yuan et al., 2013] Changhe Yuan, Brandon M Malone, et al. Learning optimal Bayesian networks: A shortest path perspective. Journal of Artificial Intelligence Research, 48:23-65, 2013.","title":"Learning optimal Bayesian networks: A shortest path perspective","authors":"Changhe Yuan, Brandon M Malone","year":"2013","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhang, 2008] Jiji Zhang. On the completeness of orienta- tion rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence, 172:1873-1896, 2008.","title":"On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias","authors":"Jiji Zhang","year":"2008","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}