Identification of conditional interventional distributions

Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Volumn , Issue , 2006, Pages 437-444

  Shpitser, Ilya ^a   Pearl, Judea ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIRECTED GRAPHS; DO-CALCULUS; IDENTIFICATION PROBLEM; INTERVENTIONAL; SEQUENTIAL DECISION MAKING; STATISTICAL KNOWLEDGE;

ARTIFICIAL INTELLIGENCE; CALCULATIONS;

PROBABILITY DISTRIBUTIONS;

EID: 46149083633     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Avin, C.; Shpitser, I.; and Pearl, J. 2005. Identifiability of path-specific effects. In International Joint Conference on Artificial Intelligence, volume 19, 357-363.
2. Halpern, J. 2000. Axiomatizing causal reasoning. Journal of A.I. Research 317-337.
3. Huang, Y., and Valtorta, M. 2006. Identifiability in causal bayesian networks: A sound and complete algorithm. In Twenty-First National Conference on Artificial Intelligence.
4. Kuroki, M., and Miyakawa, M. 1999. Identifiability criteria for causal effects of joint interventions. Journal of Japan Statistical Society 29:105-117.
5. Pearl, J., and Robins, J. M. 1995. Probabilistic evaluation of sequential plans from causal models with hidden variables. In Uncertainty in Artificial Intelligence, volume 11, 444-453.
6. Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems. Morgan and Kaufmann, San Mateo.
7. Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82(4):669-709.
8. Pearl, J. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press.
9. Pearl, J. 2001. Direct and indirect effects. In Proceedings of UAI-01, 411-420.
10. Shpitser, I., and Pearl, J. 2006. Identification of joint interventional distributions in recursive semi-markovian causal models. In Twenty-First National Conference on Artificial Intelligence.
11. Spirtes, P.; Glymour, C.; and Scheines, R. 1993. Causation, Prediction, and Search. Springer Verlag, New York.
12. Tian, J., and Pearl, J. 2002. A general identification condition for causal effects. In Eighteenth National Conference on Artificial Intelligence, 567-573.
13. Tian, J. 2002. Studies in Causal Reasoning and Learning. Ph.D. Dissertation, Department of Computer Science, University of California, Los Angeles.
14. Tian, J. 2004. Identifying conditional causal effects. In Conference on Uncertainty in Artificial Intelligence (UAI).