A sound and complete algorithm for learning causal models from relational data

Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013

Volumn , Issue , 2013, Pages 371-380

  Maier, Marc ^a   Marazopoulou, Katerina ^a   Arbour, David ^a   Jensen, David ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIVARIATE; CAUSAL BAYESIAN NETWORK; CAUSAL MODEL; CONDITIONAL INDEPENDENCES; LEARNING STRUCTURE; RELATIONAL DATA; RELATIONAL MODEL; SOUND AND COMPLETE;

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS;

LEARNING ALGORITHMS;

EID: 84888165194     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. W. L. Buntine. Operations for learning with graphical models. Journal of Artificial Intelligence Research, 2: 159-225, 1994.
2. T. Claassen and T. Heskes. A Bayesian approach to constraint based causal inference. In Proceedings of the Twenty-Eighth Annual Conference on Uncertainty in Artificial Intelligence, pages 207-216, 2012.
3. D. Colombo and M. H. Maathuis. A Modification of the PC Algorithm Yielding Order-Independent Skeletons. arXiv preprint arXiv: 1211.3295, 2012.
4. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In International Joint Conference on Artificial Intelligence, Volume 16, pages 1300-1309, 1999.
5. L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning probabilistic models of link structure. Journal of Machine Learning Research, 3: 679-707, 2002.
6. L. Getoor, N. Friedman, D. Koller, A. Pfeffer, and B. Taskar. Probabilistic relational models. In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning, pages 129-174. MIT Press, Cambridge, MA, 2007.
7. W. R. Gilks, A. Thomas, and D. J. Spiegelhalter. A language and program for complex Bayesian modeling. The Statistician, 43: 169-177, 1994.
8. D. Heckerman, C. Meek, and D. Koller. Probabilistic entity-relationship models, PRMs, and plate models. In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning, pages 201-238. MIT Press, Cambridge, MA, 2007.
9. P. O. Hoyer, D. Janzing, J. M. Mooij, J. Peters, and B. Schölkopf. Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems 22, pages 689-696, 2008.
10. D. Jensen and J. Neville. Linkage and autocorrelation cause feature selection bias in relational learning. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 259-266, 2002.
11. S. Kramer, N. Lavrač, and P. Flach. Propositionalization approaches to relational data mining. In S. Džeroski and N. Lavrač, editors, Relational Data Mining, pages 262-286. Springer-Verlag, New York, NY, 2001.
12. M. Maier, B. Taylor, H. Oktay, and D. Jensen. Learning causal models of relational domains. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, pages 531-538, 2010.
13. M. Maier, K. Marazopoulou, and D. Jensen. Reasoning about Independence in Probabilistic Models of Relational Data. arXiv preprint arXiv: 1302.4381, 2013.
14. D. Margaritis and S. Thrun. Bayesian network induction via local neighborhoods. In Advances in Neural Information Processing Systems 12, pages 505-511, 1999.
15. C. Meek. Causal inference and causal explanation with background knowledge. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 403-410, 1995.
16. J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, NY, 2000.
17. J.-P. Pellet and A. Elisseeff. Using Markov blankets for causal structure learning. Journal of Machine Learning Research, 9: 1295-1342, 2008.
18. J. Peters, D. Janzing, and B. Schölkopf. Causal inference on discrete data using additive noise models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12): 2436-2450, 2011.
19. M. J. Rattigan, M. Maier, and D. Jensen. Relational blocking for causal discovery. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pages 145-151, 2011.
20. S. Shimizu, P. O. Hoyer, A. Hyvarinen, and A. Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7: 2003-2030, 2006.
21. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search. MIT Press, Cambridge, MA, 2nd edition, 2000.