Causal transportability of experiments on controllable subsets of variables: Z-transportability

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

Volumn , Issue , 2013, Pages 361-370

  Lee, Sanghack ^a   Honavar, Vasant ^a  

^a Iowa State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARBITRARY SUBSETS; DO-CALCULUS; OBSERVABLE VARIABLES; OBSERVATIONAL DATA; SOUND AND COMPLETE; TARGET DOMAIN;

ARTIFICIAL INTELLIGENCE; ESTIMATION; SET THEORY;

EXPERIMENTS;

EID: 84888168872     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Bareinboim, E., and Pearl, J. 2012a. Causal inference by surrogate experiments: z-identifiability. In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 113-120. AUAI Press.
2. Bareinboim, E., and Pearl, J. 2012b. Transportability of causal effects: Completeness results. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 698-704. AAAI Press.
3. Bareinboim, E., and Pearl, J. 2013a. Causal transportability of limited experiments. In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. AAAI Press. To appear.
4. Bareinboim, E., and Pearl, J. 2013b. Meta-transportability of causal effects: A formal approach. In Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics. To appear.
5. Galles, D., and Pearl, J. 1995. Testing identifiability of causal effects. In Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, 185-195. Morgan Kaufmann.
6. Huang, Y., and Valtorta, M. 2006. Pearl's calculus of intervention is complete. In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, 217-224. AUAI Press.
7. Lee, S., and Honavar, V. 2013. m-transportability: Transportability of a causal effect from multiple environments. In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. AAAI Press. To appear.
8. Pearl, J., and Bareinboim, E. 2011. Transportability of causal and statistical relations: A formal approach. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 247-254. AAAI Press.
9. Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82(4): 669-688.
10. Pearl, J. 2000. Causality: models, reasoning, and inference. New York, NY, USA: Cambridge University Press.
11. Pearl, J. 2012. The do-calculus revisited. In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 4-11. AUAI Press.
12. Shpitser, I., and Pearl, J. 2006a. Identification of conditional interventional distributions. In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, 437-444. AUAI Press.
13. Shpitser, I., and Pearl, J. 2006b. Identification of joint interventional distributions in recursive semimarkovian causal models. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, 1219-1226. AAAI Press.
14. Tian, J., and Pearl, J. 2002. A general identification condition for causal effects. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, 567-573. AAAI Press / The MIT Press.
15. Tian, J. 2004. Identifying conditional causal effects. In Proceedings of the Twentieth Conference in Uncertainty in Artificial Intelligence, 561-568. AUAI Press.