Transportability of causal and statistical relations: A formal approach

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2011, Pages 247-254

  Pearl, Judea ^a   Bareinboim, Elias ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORMAL APPROACH; FORMAL REPRESENTATIONS; STATISTICAL RELATIONS;

EXPERIMENTS; KNOWLEDGE REPRESENTATION;

ARTIFICIAL INTELLIGENCE;

EID: 80055025650     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Bareinboim, E., and Pearl, J. 2011. Controlling selection bias in causal inference. Technical Report R-381, 〈http://ftp.cs.ucla.edu/pub/stat.ser/ r381.pdf〉 (also, in this proceedings), Department of Computer Science, University of California, Los Angeles.
2. Daume III, H., and Marcu, D. 2006. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research 26:101-126. (Pubitemid 44452208)
3. Dawid, A. 2002. Influence diagrams for causal modelling and inference. International Statistical Review 70:161-189.
4. Koller, D., and Friedman, N. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press.
5. Pearl, J., and Bareinboim, E. 2011. Transportability across studies: A formal approach. Technical Report R-372, 〈http://ftp.cs.ucla.edu/pub/stat- ser/r372.pdf〉, Department of Computer Science, University of California, Los Angeles.
6. Pearl, J., and Paz, A. 2010. Confounding equivalence in causal equivalence. In Grünwald, P., and Spirtes, P., eds., Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence. Corvallis, OR: AUAI. 433-441.
7. Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82(4):669-710.
8. Pearl, J. 2009. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press, 2nd edition.
9. Shadish, W.; Cook, T.; and Campbell, D. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton-Mifflin, second edition.
10. Shpitser, I., and Pearl, J. 2006. Identification of conditional interventional distributions. In Dechter, R., and Richardson, T., eds., Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence. Corvallis, OR: AUAI Press. 437-444.
11. Spirtes, P.; Glymour, C.; and Scheines, R. 2000. Causation, Prediction, and Search. Cambridge, MA: MIT Press, 2nd edition.
12. Storkey, A. 2009. When training and test sets are different: characterizing learning transfer. In Candela, J.; Sugiyama, M.; Schwaighofer, A.; and Lawrence, N., eds., Dataset Shift in Machine Learning. Cambridge, MA: MIT Press. 3-28.