Transportability from multiple environments with limited experiments

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Bareinboim, Elias ^a   Lee, Sanghack ^b   Honavar, Vasant ^b   Pearl, Judea ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENT DOMAINS; HETEROGENEOUS DOMAINS; MULTIPLE DOMAINS; SYMBOLIC DERIVATIONS; TARGET DOMAIN;

EXPERIMENTS;

EID: 84899031657     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. J. Pearl and E. Bareinboim. Transportability of causal and statistical relations: A formal approach. In W. Burgard and D. Roth, editors, Proceedings of the Twenty-Fifth National Conference on Artificial Intelligence, pages 247-254. AAAI Press, Menlo Park, CA, 2011.
2. D. Campbell and J. Stanley. Experimental and Quasi-Experimental Designs for Research. Wadsworth Publishing, Chicago, 1963.
3. C. Manski. Identification for Prediction and Decision. Harvard University Press, Cambridge, Massachusetts, 2007.
4. L. V. Hedges and I. Olkin. Statistical Methods for Meta-Analysis. Academic Press, January 1985.
5. W.R. Shadish, T.D. Cook, and D.T. Campbell. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton-Mifflin, Boston, second edition, 2002.
6. S. Morgan and C. Winship. Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research). Cambridge University Press, New York, NY, 2007.
7. J. Pearl. Causal diagrams for empirical research. Biometrika, 82(4):669-710, 1995.
8. P. Spirtes, C.N. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, Cambridge, MA, 2nd edition, 2000.
9. J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, 2000. 2nd edition, 2009.
10. D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
11. E. Bareinboim and J. Pearl. Transportability of causal effects: Completeness results. In J. Hoffmann and B. Selman, editors, Proceedings of the Twenty-Sixth National Conference on Artificial Intelligence, pages 698-704. AAAI Press, Menlo Park, CA, 2012.
12. E. Bareinboim and J. Pearl. Causal transportability with limited experiments. In M. desJardins and M. Littman, editors, Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence, pages 95-101, Menlo Park, CA, 2013. AAAI Press.
13. S. Lee and V. Honavar. Causal transportability of experiments on controllable subsets of variables: z-transportability. In A. Nicholson and P. Smyth, editors, Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), pages 361-370. AUAI Press, 2013
14. E. Bareinboim and J. Pearl. Meta-transportability of causal effects: A formal approach. In C. Carvalho and P. Ravikumar, editors, Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS), pages 135-143. JMLR W&CP 31, 2013.
15. S. Lee and V. Honavar. m-transportability: Transportability of a causal effect from multiple environments. In M. desJardins and M. Littman, editors, Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence, pages 583-590, Menlo Park, CA, 2013. AAAI Press.
16. H. Daume III and D. Marcu. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26:101-126, 2006.
17. A.J. Storkey. When training and test sets are different: characterising learning transfer. In J. Candela, M. Sugiyama, A. Schwaighofer, and N.D. Lawrence, editors, Dataset Shift in Machine Learning, pages 3-28. MIT Press, Cambridge, MA, 2009.
18. B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij. On causal and anticausal learning. In J Langford and J Pineau, editors, Proceedings of the 29th International Conference on Machine Learning (ICML), pages 1255-1262, New York, NY, USA, 2012. Omnipress.
19. K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang. Domain adaptation under target and conditional shift. In Proceedings of the 30th International Conference on Machine Learning (ICML). JMLR: W&CP volume 28, 2013.
20. E. Bareinboim and J. Pearl. Causal inference by surrogate experiments: z-identifiability. In N. Freitas and K. Murphy, editors, Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI), pages 113-120. AUAI Press, 2012.
21. M. Kuroki and M. Miyakawa. Identifiability criteria for causal effects of joint interventions. Journal of the Royal Statistical Society, 29:105-117, 1999.
22. J. Tian and J. Pearl. A general identification condition for causal effects. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pages 567-573. AAAI Press/The MIT Press, Menlo Park, CA, 2002.
23. I. Shpitser and J. Pearl. Identification of joint interventional distributions in recursive semi-Markovian causal models. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pages 1219-1226. AAAI Press, Menlo Park, CA, 2006.