What counterfactuals can be tested

Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007

Volumn , Issue , 2007, Pages 352-359

  Shpitser, Ilya ^a   Pearl, Judea ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COUNTERFACTUALS; EXPERIMENTAL DATA; PHYSICAL EXPERIMENTS; SCIENTIFIC DISCOURSE;

ARTIFICIAL INTELLIGENCE;

EID: 77649296605     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

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3. Balke, A., and Pearl, J. 1994b. Probabilistic evaluation of counterfactual queries. In Proceedings of AAAI-94, 230-237.
4. Dawid, A. P. 2000. Causal inference without counterfactuals. Journal of the American Statistical Association 95:407-448.
5. Galles, D., and Pearl, J. 1998. An axiomatic characterization of causal counterfactuals. Foundation of Science 3:151-182.
6. Halpern, J. 2000. Axiomatizing causal reasoning. Journal of A.I. Research 317-337.
7. Huang, Y., and Valtorta, M. 2006. Identifiability in causal bayesian networks: A sound and complete algorithm. In Twenty-First National Conference on Artificial Intelligence.
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10. Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems. Morgan and Kaufmann, San Mateo.
11. Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82(4):669-709.
12. Pearl, J. 2000a. Causality: Models, Reasoning, and Inference. Cambridge University Press.
13. Pearl, J. 2000b. The logic of counterfactuals in causal inference (discussion of 'causal inference without counterfactuals' by a.p. dawid). Journal of American Statistical Association 95(450):428-435.
14. Shpitser, I., and Pearl, J. 2006a. Identification of conditional interventional distributions. In Uncertainty in Artificial Intelligence, volume 22.
15. Shpitser, I., and Pearl, J. 2006b. Identification of joint interventional distributions in recursive semi-markovian causal models. In Twenty-First National Conference on Artificial Intelligence.
16. Spirtes, P.; Glymour, C; and Schemes, R. 1993. Causation, Prediction, and Search. Springer Verlag, New York.
17. Tian, J., and Pearl, J. 2002. A general identification condition for causal effects. In Eighteenth National Conference on Artificial Intelligence, 567-573.
18. Tian, J. 2002. Studies in Causal Reasoning and Learning. Ph.D. Dissertation, Department of Computer Science, University of California, Los Angeles.