A new characterization of the experimental implications of causal bayesian networks

Proceedings of the National Conference on Artificial Intelligence

Volumn , Issue , 2002, Pages 574-579

  Tian, Jin ^a   Pearl, Judea ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPHIC METHODS; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES;

BAYESIAN NETWORKS;

COMPUTER NETWORKS;

EID: 0036931897     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Dawid, A. 2001. Influence diagrams for causal modelling and inference. International Statistical Review. Forthcoming.
2. Galles, D., and Pearl, J. 1998. An axiomatic characterization of causal counterfactuals. Foundations of Science 3(1):151-182.
3. Halpern, J. 1998. Axiomatizing causal reasoning. In Cooper, G., and Moral, S., eds., Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann. 202-210.
4. Heckerman, D., and Shachter, R. 1995. Decision-theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research 3:405-430.
5. Kiiveri, H.; Speed, T.; and Carlin, J. 1984. Recursive causal models. Journal of Australian Math Society 36:30-52.
6. Lauritzen, S. 2000. Graphical models for causal inference. In Barndorff-Nielsen, O.; Cox, D.; and Kluppelberg, C., eds., Complex Stochastic Systems. London/Boca Raton: Chapman and Hall/CRC Press, chapter 2, 67-112.
7. Pearl, J. 1988. Probabilistic Reasoning in Intelligence Systems. San Mateo, CA: Morgan Kaufmann.
8. Pearl, J. 1993. Comment: Graphical models, causality, and intervention. Statistical Science 8:266-269.
9. Pearl, I. 1995. Causal diagrams for experimental research. Biometrika 82:669-710.
10. Pearl, J. 2000. Causality: Models, Reasoning, and Inference. NY: Cambridge University Press.
11. Reichenbach, H. 1956. The Direction of Time. Berkeley: University of California Press.
12. Robins, J. 1987. A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. Journal of Chronic Diseases 40(Suppl 2):139S-161S.
13. Spirtes, P.; Glymour, C.; and Schemes, R. 1993. Causation, Prediction, and Search. New York: Springer-Verlag.
14. Tian, J., and Pearl, J. 2002. On the Identification of causal effects. Technical Report R-290-L, Department of Computer Science, University of California, Los Angeles.