Learning Causal Models of Relational Domains

Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Volumn , Issue , 2010, Pages 531-538

  Maier, Marc ^a   Taylor, Brian ^a   Oktay, Hüseyin ^a   Jensen, David ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMICS; CAUSAL DEPENDENCIES; CAUSAL MODELING; EFFECTIVE LEARNING; KNOWLEDGE-REPRESENTATION; LEARN+; OBSERVATIONAL DATA; RELATIONAL REPRESENTATIONS; STATE OF THE ART; THEORETICAL INNOVATIONS;

KNOWLEDGE REPRESENTATION;

EID: 84891372744     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Berkson, J. 1946. Limitations of the application of fourfold table analysis to hospital data. Biometrics Bulletin 2(3):47-53.
2. Chickering, D. M. 1996. Learning Bayesian networks is NP-complete. In Fisher, D. H., and Lenz, H.-J., eds., Learning from Data: Artificial Intelligence and Statistics V. Springer. 121-130.
3. Friedkin, N. E. 1998. A Structural Theory of Social Influence. New York, NY: Cambridge University Press.
4. Getoor, L.; Friedman, N.; Koller, D.; Pfeffer, A.; and Taskar, B. 2007. Probabilistic relational models. In Getoor, L., and Taskar, B., eds., Introduction to Statistical Relational Learning. Cambridge, MA: MIT Press. 129-174.
5. Heckerman, D.; Meek, C.; and Koller, D. 2007. Probabilistic entity-relationship models, PRMs, and plate models. In Getoor, L., and Taskar, B., eds., Introduction to Statistical Relational Learning. Cambridge, MA: MIT Press. 201-238.
6. Holland, P. W., and Rubin, D. B. 1988. Causal inference in retrospective studies. Evaluation Review 12(3):203-231.
7. Jensen, D.; Neville, J.; and Hay, M. 2003. Avoiding bias when aggregating relational data with degree disparity. In Proceedings of the 20th ICML, 274-281.
8. Kramer, S.; Lavrač, N.; and Flach, P. 2001. Propositionalization approaches to relational data mining. In Džeroski, S., and Lavrač, N., eds., Relational Data Mining. New York, NY: Springer-Verlag. 262-286.
9. Pearl, J., and Verma, T. 1991. A theory of inferred causation. In Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, volume 11, 441-452.
10. Pearl, J. 2000. Causality: Models, Reasoning, and Inference. New York, NY: Cambridge University Press.
11. Richardson, M., and Domingos, P. 2006. Markov logic networks. Machine Learning 62(1-2):107-136.
12. Spirtes, P.; Glymour, C.; and Scheines, R. 2000. Causation, Prediction and Search. Cambridge, MA: MIT Press, 2nd edition.
13. Taskar, B.; Abbeel, P.; and Koller, D. 2002. Discriminative probabilistic models for relational data. In Proceedings of the 18th UAI, 485-492.
14. Tsamardinos, I.; Brown, L. E.; and Aliferis, C. F. 2006. The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning 65(1):31-78.