A hybrid causal search algorithm for latent variable models

Journal of Machine Learning Research

Volumn 52, Issue 2016, 2016, Pages 368-379

  Ogarrio, Juan Miguel ^a   Spirtes, Peter ^a   Ramsey, Joe ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPHIC METHODS; LEARNING ALGORITHMS;

CAUSAL GRAPH; CAUSAL MODEL; CONSTRAINT-BASED; LATENT VARIABLE MODELS; SEARCH ALGORITHMS; SMALL SAMPLES; SYNTHETIC DATA;

GRAPH ALGORITHMS;

EID: 85087332357     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (14)

1. D.M. Chickering. Optimal structure identification with greedy search. Journal of Machine Learning Research, 3:507-554, 2002.
2. T. Claasen, J. M. Mooij, and T. Heskes. Learning sparse causal models is not np-hard. In P. S. Ann Nicholson, editor, Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13), volume 29, pages 172-181. AUAI Press, 2013.
3. D. Colombo, M. H. Maathuis, M. Kalisch, and T. Richardson. Learning high-dimensional directed acyclic graphs with latent and selection variables. Annals of Statistics, 40(1):294-321, 2012.
4. G. Elidan and N. Friedman. Learning hidden variable networks: The information bottleneck approach. J Mach Learn Res, 6:81-127, 2005.
5. N. Friedman. The bayesian structural em algorithm. In G. Cooper and S. Moral, editors, UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pages 129- 137, Madison, WI, 1998. Morgan Kaurmann.
6. P. O. Hoyer, S. Shimizu, A. Kerminen, and M. Palviainen. Estimation of causal effects using linear non-gaussian causal models with hidden variables. International Journal of Approximate Reasoning, 49(2):362-378, 2008.
7. A. Hyttinen, P. Hoyer, F. Eberhardt, and M. Jarvisalo. Discovering cyclic causal models with latent variables: A general sat-based procedure. In P. S. Ann Nicholson, editor, Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13), volume 29, pages 301-310, Corvallis, Oregon, 2013. AUAI Press.
8. E. Kummerfeld, J. Ramsey, R. Yang, P. Spirtes, and R. Scheines. Causal clustering for 2-factor measurement models. In T. Calders, F. Esposito, E. Hllermeier, and R. Meo, editors, Machine Learning and Knowledge Discovery in Databases, pages 34-49. Springer, 2014.
9. A. Murray-Watters and C. Glymour. What is going on inside the arrows? discovering the hidden springs in causal models. Philosophy of Science, 82(4):pp. 556-586, 2015.
10. P. Nandy, A. Hauser, and M. Maathuis. High-dimensional consistency in score-based and hybrid structure learning. 2016. URL http://arxiv.org/pdf/1507.02608v3.pdf.
11. J. D. Ramsey. Scaling up greedy equivalence search for continuous variables. CoRR, abs/1507.07749, 2015. URL http://arxiv.org/abs/1507.07749.
12. R. Silva, R. Scheines, C. Glymour, and P. Spirtes. Learning the structure of linear latent variable models. J Mach Learn Res, 7:191-246, 2006.
13. P. Spirtes, T. RIchardson, and C. Meek. Causal discovery in the presence of latent variables and selection bias. In G. Cooper and C. Glymour, editors, Computation, Causality, and Discovery, pages 211-252. AAAI Press, 1999.
14. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search, SecondEdition(Adaptive Computation and Machine Learning). The MIT Press, 2001.