Transportability of causal effects: Completeness results

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2012, Pages 698-704

  Bareinboim, Elias ^a   Pearl, Judea ^a  

^a Engineering IV   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CAUSAL RELATIONS; STATISTICAL INFORMATION; SUFFICIENT CONDITIONS; TARGET DOMAIN;

EXPERIMENTS;

ARTIFICIAL INTELLIGENCE;

EID: 84868282548     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Adelman, L. 1991. Experiments, quasi-experiments, and case studies: A review of empirical methods for evaluating decision support systems. Systems, Man and Cybernetics, IEEE Transactions on 21(2):293-301.
2. Bareinboim, E., and Pearl, J. 2012a. Transportability of causal effects: Completeness results. Technical Report TR-390, Cognitive Systems Laboratory, Department of Computer Science, UCLA.
3. Bareinboim, E., and Pearl, J. 2012b. Controlling selection bias in causal inference. In Girolami, M., and Lawrence, N., eds., Proceedings of The Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012), JMLR (22), 100-108.
4. Campbell, D., and Stanley, J. 1963. Experimental and Quasi-Experimental Designs for Research. Chicago: Wadsworth Publishing.
5. Glass, G. V. 1976. Primary, secondary, and meta-analysis of research. Educational Researcher 5(10):pp. 3-8.
6. Hedges, L. V., and Olkin, I. 1985. Statistical Methods for Meta-Analysis. Academic Press.
7. Huang, Y., and Valtorta, M. 2006. Identifiability in causal bayesian networks: A sound and complete algorithm. In Proceedings of the Twenty-First National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press. 1149-1156.
8. Koller, D., and Friedman, N. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press.
9. Kuroki, M., and Miyakawa, M. 1999. Identifiability criteria for causal effects of joint interventions. Journal of the Royal Statistical Society 29:105-117.
10. Manski, C. 2007. Identification for Prediction and Decision. Cambridge, Massachusetts: Harvard University Press.
11. Owen, A. B. 2009. Karl pearsons meta-analysis revisited. Annals of Statistics 37(6B):3867-3892.
12. Pearl, J., and Bareinboim, E. 2011. Transportability of causal and statistical relations: A formal approach. In Proceedings of the Twenty-Fifth National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press. 247-254.
13. Pearl, J., and Verma, T. 1991. A theory of inferred causation. In Allen, J.; Fikes, R.; and Sandewall, E., eds., Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference. San Mateo, CA: Morgan Kaufmann. 441-452.
14. Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82(4):669-710.
15. Pearl, J. 2000. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press. Second ed., 2009.
16. Pearl, J. 2001. Direct and indirect effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann. 411-420.
17. Pearl, J. 2009. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press, second edition.
18. Pearl, J. 2012. The mediation formula: A guide to the assessment of causal pathways in nonlinear models. In Berzuini, C.; Dawid, P.; and Bernardinell, L., eds., Causality: Statistical Perspectives and Applications. New York: Wiley. Chapter 12.
19. Shadish, W.; Cook, T.; and Campbell, D. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton-Mifflin, second edition.
20. Shpitser, I., and Pearl, J. 2006. Identification of joint interventional distributions in recursive semi-Markovian causal models. In Proceedings of the Twenty-First National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press. 1219-1226.
21. Spirtes, P.; Glymour, C.; and Scheines, R. 2001. Causation, Prediction, and Search. Cambridge, MA: MIT Press, 2nd edition.
22. Tian, J., and Pearl, J. 2002. A general identification condition for causal effects. In Proceedings of the Eighteenth National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press/The MIT Press. 567-573.
23. Verma, T., and Pearl, J. 1990. Equivalence and synthesis of causal models. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, 220-227.
24. Also in P. Bonissone, M. Henrion, L.N. Kanal and J.F. Lemmer (Eds.), Uncertainty in Artificial Intelligence 6, Elsevier Science Publishers, B.V., 255-268, 1991.