A general identification condition for causal effects

Proceedings of the National Conference on Artificial Intelligence

Volumn , Issue , 2002, Pages 567-573

  Tian, Jin ^a   Pearl, Judea ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENCODING (SYMBOLS); GRAPHIC METHODS; MATHEMATICAL MODELS; PROBLEM SOLVING;

CAUSAL EFFECTS;

DATA PROCESSING;

EID: 0036927237     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Galles, D., and Pearl, J. 1995. Testing identifiability of causal effects. In Besnard, P., and Hanks, S., eds., Uncertainty in Artificial Intelligence 11. San Francisco: Morgan Kaufmann. 185-195.
2. Greenland, S.; Pearl, J.; and Robins, J. 1999. Causal diagrams for epidemiologic research. Epidemiology 10:37-48.
3. 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.
4. Pearl, J. 1988. Probabilistic Reasoning in Intelligence Systems. San Mateo, CA: Morgan Kaufmann.
5. Pearl, J. 1993. Comment: Graphical models, causality, and intervention. Statistical Science 8:266-269.
6. Pearl, J. 1995. Causal diagrams for experimental research. Biometrika 82:669-710.
7. Pearl, J. 2000. Causality: Models, Reasoning, and Inference. NY: Cambridge University Press.
8. 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.
9. Spirtes, P.; Glymour, C.; and Schemes, R. 1993. Causation, Prediction, and Search. New York: Springer-Verlag.
10. 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.