Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables

Journal of Machine Learning Research

Volumn 15, Issue , 2011, Pages 3-15

  Tillman, Robert E ^a   Spirtes, Peter ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL INDEPENDENCES; DATA SETS; FINITE SAMPLES; GRAPHICAL MODEL; PROBABILISTIC GRAPHICAL MODELS; STATISTICAL ERRORS;

ARTIFICIAL INTELLIGENCE; EQUIVALENCE CLASSES; GRAPHIC METHODS; INFERENCE ENGINES; SAMPLING;

DATA PROCESSING;

EID: 84862301167     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (16)

1. D. Danks. Scientific coherence and the fusion of experimental results. The British Journal for the Philosophy of Science, 56:791-807, 2005.
2. R. A. Fisher. Statistical Methods for Research Workers. Oliver and Boyd, London, 11th edition, 1950.
3. N. Friedman. The Bayesian structural EM algorithm. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998.
4. P. O. Hoyer, D. Janzing, J. M. Mooij, J. Peters, and B. Schölkopf. Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems 21, 2009.
5. G. Melançon, I. Dutour, and M. Bousquet-Mélou. Random generation of dags for graph drawing. Technical Report INS-R0005, Centre for Mathematics and Computer Sciences, 2000.
6. J. D. Ramsey, S. J. Hanson, C. Hanson, Y. O. Halchenko, R. A. Poldrack, and C. Glymour. Six problems for causal inference from fMRI. NeuroImage, 49(2):1545-1558, 2010.
7. T. Richardson and P. Spirtes. Ancestral graph markov models. Annals of Statistics, 30(4):962-1030, 2002.
8. S. Shimizu, P. Hoyer, A. Hyvärinen, and A. Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7:1003-2030, 2006.
9. P. Spirtes, C. Meek, and T. Richardson. Causal inference in the presence of latent variables and selection bias. In C. Glymour and G. Cooper, editors, Computation, Causation and Discovery, pages 211-252. MIT Press, 1999.
10. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, 2nd edition, 2000.
11. R. E. Tillman, D. Danks, and C. Glymour. Integerating locally learned causal structures with overlapping variables. In Advances in Neural Information Processing Systems 21, 2009.
12. R. E. Tillman. Structure learning with independent non-identically distributed data. In Proceedings of the 26th International Conference on Machine Learning, 2009.
13. T. S. Verma and J. Pearl. Equivalence and synthesis of causal models. In Proceedings of the 6th Conference on Uncertainty in Artificial Intelligence, 1991.
14. J. Zhang. Causal Inference and Reasoning in Causally Insufficient Systems. PhD thesis, Carnegie Mello University, 2006.
15. J. Zhang. A characterization of markov equivalence classes for causal models with latent variables. In Proceedings of Uncertainty in Artificial Intelligence, 2007.
16. J. Zhang. Causal reasoning with ancestral graphs. Journal of Machine Learning Research, 9:1437-1474, 2008.