Identifying dynamic sequential plans

Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

Volumn , Issue , 2008, Pages 554-561

  Tian, Jin ^a  

^a Iowa State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CAUSAL BAYESIAN NETWORK; IDENTIFICATION ALGORITHMS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE;

BAYESIAN NETWORKS;

EID: 77953722981     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. A.P. Dawid and V. Didelez. Identifying the consequences of dynamic treatment strategies. Technical report, Department of Statistical Science, University College London, UK, 2005.
2. A.P. Dawid. Inuence diagrams for causal modelling and inference. International Statistical Review, 70(2), 2002.
3. V. Didelez, A.P. Dawid, and S. Geneletti. Direct and indirect effects of sequential treatments. In Proceedings of the 22nd Annual Conference on Uncertainty in Artifical Intelligence, pages 138-146, 2006.
4. D. Heckerman and R. Shachter. Decision-theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research, 3:405-430, 1995.
5. Y. Huang and M. Valtorta. Identifiability in causal bayesian networks: A sound and complete algorithm. In Proceedings of the Twenty-First National Conference on Articial Intelligence, pages 1149-1154, Menlo Park, CA, July 2006. AAAI Press. (Pubitemid 44705430)
6. S. Lauritzen. Graphical models for causal inference. In O.E. Barndor-Nielsen, D. Cox, and C. Kluppelberg, editors, Complex Stochastic Systems, chapter 2, pages 67-112. Chapman and Hall/CRC Press, London/Boca Raton, 2000.
7. J. Pearl and J.M. Robins. Probabilistic evaluation of sequential plans from causal models with hidden variables. In P. Besnard and S. Hanks, editors, Uncertainty in Artificial Intelligence 11, pages 444-453. Morgan Kaufmann, San Francisco, 1995.
8. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA, 1988.
9. J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, NY, 2000.
10. J. Pearl. Comment on identifying conditional plans. 2006. http://www.mii.ucla.edu/causality/?p=36#comments.
11. J.M. Robins. A new approach to causal inference in mortality studies with a sustained exposure period - applications to control of the healthy workers survivor effect. Mathematical Modeling, 7:1393-1512, 1986.
12. J.M. Robins. 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, 1987.
13. I. Shpitser and J. Pearl. Identification of conditional interventional distributions. In R. Dechter and T.S. Richardson, editors, Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, pages 437-444. AUAI Press, July 2006.
14. I. Shpitser and J. Pearl. Identification of joint interventional distributions in recursive semi-markovian causal models. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pages 1219-1226, Menlo Park, CA, July 2006. AAAI Press. (Pubitemid 44705441)
15. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. Springer-Verlag, New York, 1993.
16. J. Tian and J. Pearl. On the Identification of causal effects. Technical Report R-290-L, Department of Computer Science, University of California, Los Angeles, 2003. http://www.cs.iastate.edu/-jtian/r290-L.pdf.
17. J. Tian. Identifying conditional causal effects. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2004.