Causal discovery in multiple models from different experiments

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Claassen, Tom ^a   Heskes, Tom ^a  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND KNOWLEDGE; CAUSAL RELATIONS; CONSTRAINT-BASED; INPUT MODELS; MODEL SET; MULTIPLE DATA SETS; MULTIPLE-MODELING; REAL-WORLD; RESEARCH PROBLEMS; SYNTHETIC INPUT;

NEURAL NETWORKS;

EID: 85161990539     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search. Cambridge, Massachusetts: The MIT Press, 2nd ed., 2000.
2. D. Chickering, "Optimal structure identification with greedy search," Journal of Machine Learning Research, vol. 3, no. 3, pp. 507-554, 2002.
3. R. Tillman, D. Danks, and C. Glymour, "Integrating locally learned causal structures with overlapping variables," in Advances in Neural Information Processing Systems, 21, 2008.
4. S. Mani, G. Cooper, and P. Spirtes, "A theoretical study of Y structures for causal discovery," in Proceedings of the 22nd Conference in Uncertainty in Artificial Intelligence, pp. 314-323, 2006.
5. J. Pearl, Causality: models, reasoning and inference. Cambridge University Press, 2000.
6. J. Zhang, "Causal reasoning with ancestral graphs," Journal of Machine Learning Research, vol. 9, pp. 1437 - 1474, 2008.
7. T. Richardson and P. Spirtes, "Ancestral graph Markov models," Ann. Stat., vol. 30, no. 4, pp. 962-1030, 2002.
8. J. Zhang, "On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias," Artificial Intelligence, vol. 172, no. 16-17, pp. 1873 -1896, 2008.
9. J. Zhang and P. Spirtes, "Detection of unfaithfulness and robust causal inference," Minds and Machines, vol. 2, no. 18, pp. 239-271, 2008.
10. P. Spirtes, C. Meek, and T. Richardson, "An algorithm for causal inference in the presence of latent variables and selection bias," in Computation, Causation, and Discovery, pp. 211-252, 1999.
11. S. Shimizu, P. Hoyer, A. Hyvärinen, and A. Kerminen, "A linear non-Gaussian acyclic model for causal discovery," Journal of Machine Learning Research, vol. 7, pp. 2003-2030, 2006.
12. P. Hoyer, D. Janzing, J. Mooij, J. Peters, and B. Schölkopf, "Nonlinear causal discovery with additive noise models," in Advances in Neural Information Processing Systems 21 (NIPS*2008), pp. 689-696, 2009.
13. J. Ide and F. Cozman, "Random generation of Bayesian networks," in Advances in Artificial Intelligence, pp. 366-376, Springer Berlin, 2002.
14. T. Claassen and T. Heskes, "Learning causal network structure from multiple (in)dependence models," in Proceedings of the Fifth European Workshop on Probabilistic Graphical Models, 2010.
15. C. Meek, "Causal inference and causal explanation with background knowledge," in UAI, pp. 403-410, Morgan Kaufmann, 1995.