Identifiability in causal bayesian networks: A sound and complete algorithm

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2006, Pages 1149-1154

  Huang, Yimin ^a   Valtorta, Marco ^a  

^a UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; DIRECTED ACYCLIC GRAPH; SEMI-MARKOVIAN MODELS;

GRAPH THEORY; LEARNING ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY; PROBLEM SOLVING;

NEURAL NETWORKS;

EID: 33750720924     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Galles, D., and Pearl, J. 1995. Testing identifiability of causal effects. In Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence(UAI-95), 185-195.
2. Huang, Y., and Valtorta, M. 2006a. On the completeness of an identifiability algorithm for semi-markovian models, TR-2006-001. Technical report, University of South Carolina Department of Computer Science, available at http://www.cse.sc.edu/mgv/reports/tr2006-001.pdf.
3. Huang, Y., and Valtorta, M. 2006b. A study of identifiability in causal Bayesian networks, TR-2006-002. Technical report, University of South Carolina Department of Computer Science, available at http://www.cse.sc.edu/mgv/reports/ tr2006-002.pdf.
4. Pearl, J., and Robins, J. M. 1995. Probabilistic evaluation of sequential plans from causal models with hidden variables. In Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence( UAI-95), 444-453.
5. Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82:669-710.
6. Pearl, J. 2000. Causality: Models, Reasoning, and Inference. New York,USA: Cambridge University Press.
7. Shpitser, I., and Pearl, J. 2006. Identification of joint interventional distributions in recursive semi-markovian causal models, R-327. Technical report, Cognitive Systems Laboratory, University of California at Los Angeles, available at http://ftp.cs.ucla.edu/pub/stat_ser/r327.pdf.
8. Tian, J., and Pearl, J. 2002a. A general identification condition for causal effects. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), 567-573.
9. Tian, J., and Pearl, J. 2002b. On the testable implications of causal models with hidden variables. In Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence(UAI-02), 519-527.
10. Tian, J., and Pearl, J. 2003. On the identification of causal effects, 290-L. Technical report, Cognitive Systems Laboratory, University of California at Los Angeles. Extended version available at http://www.cs.iastate.edu/jtian/ r290-L.pdf.
11. Verma, T. S. 1993. Graphical aspects of causal models, R-191. Technical report, Cognitive Systems Laboratory, University of California at Los Angeles.