On the number of experiments suficient and in the worst case necessary to identify all causal relations among N variables

Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005

Volumn , Issue , 2005, Pages 178-184

  Eberhardt, Frederick ^a   Glymour, Clark ^a   Scheines, Richard ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CAUSAL RELATIONS; LARGE N; LATENT VARIABLE; NO FEEDBACKS; SAMPLE SELECTION BIAS; UPPER BOUND;

ARTIFICIAL INTELLIGENCE;

EID: 38049120159     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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2. F. Eberhardt, R. Scheines, and C. Glymour (2004). N-1 Experiments Sufice to Determine the Causal Relations Among N Vairables. In Department of Philosophy, Carnegie Mellon University Technical Report CMU-PHIL-161.
3. C. Meek, (1996). Ph.D Thesis, Department of Philosophy, Carnegie Mellon University
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8. P. Spirtes, C. Meek, and T. Richardson, (1999). An Algorithm for Causal Inference in the Presence of Latent Variables and Selection Bias in Computation, Causation and Discovery, 1999; MIT Press.
9. P. Spirtes, C. Glymour, and R. Scheines, (2000). Causation, Prediction, and Search, 2nd ed. New York, N.Y.: MIT Press.
10. S. Tong, and D. Koller, (2001). Active Learning for Structure in Bayesian Networks, Proceedings of the International Joint Conference on Artificial Intelligence.