Testing edges by truncations

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 1957-1963

  Shpitser, Ilya ^a   Richardson, Thomas S ^b   Robins, James M ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CAUSAL DIAGRAM; CONDITIONAL INDEPENDENCE TESTS; CONDITIONAL INDEPENDENCES; INTERVENTIONAL; JOINT DISTRIBUTIONS; LATENT VARIABLE; VARIABLE PAIR;

ARTIFICIAL INTELLIGENCE;

EID: 77953488455     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Gregory F. Cooper and Tom Dietterich. A Bayesian method for the induction of probabilistic networks from data. In Machine Learning, pages 309-347, 1992.
2. Judea Pearl and J. M. Robins. Probabilistic evaluation of sequential plans from causal models with hidden variables. In Uncertainty in Artificial Intelligence, volume 11, pages 444-453, 1995.
3. Judea Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan and Kaufmann, San Mateo, 1988.
4. Judea Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.
5. Thomas Richardson and Peter Spirtes. Ancestral graph Markov models. Annals of Statistics, 30:962-1030, 2002.
6. James M. Robins. A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Mathematical Modelling, 7:1393-1512, 1986.
7. James M. Robins. Testing and estimation of direct effects by reparameterizing directed acyclic graphs with structural nested models. Computation, Causation, and Discovery, pages 349-405, 1999.
8. Ilya Shpitser and Judea Pearl. Identification of conditional interventional distributions. In Uncertainty in Artificial Intelligence, volume 22, 2006.
9. Ilya Shpitser and Judea Pearl. Identification of joint interventional distributions in recursive semi-Markovian causal models. In Twenty-First National Conference on Artificial Intelligence, 2006.
10. Ilya Shpitser and Judea Pearl. Dormant independence. In Proceedings of the Twenty-Third Conference on Artificial Intelligence, pages 1081-1087, 2008.
11. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. Springer Verlag, New York, 1993.
12. J. Suzuki. A construction of Bayesian networks from databases based on an mdl scheme. In UAI 93, pages 266-273, 1993.
13. Jin Tian and Judea Pearl. A general identification condition for causal effects. In Eighteenth National Conference on Artificial Intelligence, pages 567-573, 2002.
14. Jin Tian and Judea Pearl. On the testable implications of causal models with hidden variables. In Proceedings of UAI-02, pages 519-527, 2002.
15. T. S. Verma and Judea Pearl. Equivalence and synthesis of causal models. Technical Report R-150, Department of Computer Science, University of California, Los Angeles, 1990.