Order-independent constraint-based causal structure learning

Journal of Machine Learning Research

Volumn 15, Issue , 2015, Pages 3741-3782

  Colombo, Diego ^a   Maathuis, Marloes H ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

CCD algorithm; Consistency; Directed acyclic graph; FCI algorithm; High dimensional data; Order dependence; PC algorithm

Indexed keywords

ALUMINUM; CLUSTERING ALGORITHMS; DIRECTED GRAPHS;

CAUSAL STRUCTURE LEARNING; CONSISTENCY; DIRECTED ACYCLIC GRAPH (DAG); GENE EXPRESSION DATA; HIGH DIMENSIONAL DATA; ORDER INDEPENDENTS; ORDER-DEPENDENCE; SIMULATION STUDIES;

GENE EXPRESSION;

EID: 84919705538     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (34)

1. R.A. Ali, T.S. Richardson, and P. Spirtes. Markov equivalence for ancestral graphs. Ann. Statist., 37:2808-2837, 2009.
2. S. A. Andersson, D. Madigan, and M. D. Perlman. A characterization of Markov equivalence classes for acyclic digraphs. Ann. Statist., 25:505-541, 1997.
3. A. Cano, M. Gómez-Olmedo, and S. Moral. A score based ranking of the edges for the PC algorithm. In Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM 2008), pages 41-48, 2008. Available at http://pgm08.cs.aau.dk/online proc.html.
4. D.M. Chickering. Learning equivalence classes of Bayesian-network structures. J. Mach. Learn. Res., 2:445-498, 2002.
5. T. Claassen, J. Mooij, and T. Heskes. Learning sparse causal models is not NP-hard. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-2013), pages 172-181. AUAI Press, Corvallis, 2013.
6. D. Colombo, M.H. Maathuis, M. Kalisch, and T.S. Richardson. Learning high-dimensional directed acyclic graphs with latent and selection variables. Ann. Statist., 40:294-321, 2012.
7. D. Dash and M.J. Druzdzel. A hybrid anytime algorithm for the construction of causal models from sparse data. In Proceedings of the Fifteenth Conference on Uncertainty on Artificial Intelligence (UAI-1999), pages 142-149. Morgan Kaufmann, San Francisco, 1999.
8. A.P. Dawid. Conditional independence for statistical operations. Ann. Statist., 8:598-617, 1980.
9. N. Harris and M. Drton. PC algorithm for nonparanormal graphical models. J. Mach. Learn. Res., 14:3365-3383, 2013.
10. T.R. Hughes, M.J. Marton, A.R. Jones, C.J. Roberts, R. Stoughton, C.D. Armour, H.A. Bennett, E. Coffey, H. Dai, Y.D. He, et al. Functional discovery via a compendium of expression profiles. Cell, 102:109-126, 2000.
11. M. Kalisch and P. Bühlmann. Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J. Mach. Learn. Res., 8:613-636, 2007.
12. M. Kalisch, B.A.G. Fellinghauer, E. Grill, M.H. Maathuis, U. Mansmann, P. Bühlmann, and G. Stucki. Understanding human functioning using graphical models. BMC Med. Res. Methodol., 10(14), 2010.
13. M. Kalisch, M. Mächler, D. Colombo, M.H. Maathuis, and P. Bühlmann. Causal inference using graphical models with the R package pcalg. J. Stat. Softw., 47(11), 2012.
14. M.H. Maathuis, M. Kalisch, and P. Bühlmann. Estimating high-dimensional intervention effects from observational data. Ann. Statist., 37:3133-3164, 2009.
15. M.H. Maathuis, D. Colombo, M. Kalisch, and P. Bühlmann. Predicting causal effects in large-scale systems from observational data. Nature Methods, 7:247-248, 2010.
16. G.M. Marchetti, M. Drton, and K. Sadeghi. R-package ggm: Graphical Gaussian Models. Available at http://cran.r-project.org, 2012.
17. C. Meek. Causal inference and causal explanation with background knowledge. In Pro-ceedings of the Eleventh Conference on Uncertainty in Artifcial Intelligence (UAI-1995), pages 403-411. Morgan Kaufmann, San Francisco, 1995.
18. N. Meinshausen and P. Bühlmann. Stability selection. J. R. Stat. Soc. Series B, 72:417-473, 2010.
19. R. Nagarajan, S. Datta, M. Scutari, M. Beggs, G. Nolen, and C. Peterson. Functional relationships between genes associated with differentiation potential of aged myogenic progenitors. Front. Physiol., 1(21), 2010.
20. J. Pearl. Causality: Models, reasoning, and inference. Cambridge University Press, Cambridge, 2000.
21. J. Pearl. Causal inference in statistics: An overview. Statistics Surveys, 3:96-146, 2009.
22. J. Ramsey, J. Zhang, and P. Spirtes. Adjacency-faithfulness and conservative causal inference. In Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelli-gence (UAI-2006). AUAI Press, Arlington, 2006.
23. T.S. Richardson. A discovery algorithm for directed cyclic graphs. In Proceedings of the 12th Annual Conference on Uncertainty in Artificial Intelligence (UAI-1996), pages 454-461. Morgan Kaufmann, San Francisco, 1996.
24. T.S. Richardson and P. Spirtes. Ancestral graph Markov models. Ann. Statist., 30:962-1030, 2002.
25. M. Singh and M. Valtorta. An algorithm for the construction of Bayesian network structures from data. In Proceedings of the 9th Annual Conference on Uncertainty in Artificial Intelligence (UAI-1993), pages 259-265. Morgan Kaufmann, San Francisco, 1993.
26. P. Spirtes. An anytime algorithm for causal inference. In Proceedings of the 8th International Workshop on Artificial Intelligence and Statistics, pages 213-221. Morgan Kaufmann, San Francisco, 2001.
27. P. Spirtes and C. Meek. Learning Bayesian networks with discrete variables from data. In Proceeding of the First International Conference on Knowledge Discovery and Data Mining (KDD-1995), pages 294-299, AAAI, Menlo Park, 1995.
28. P. Spirtes, C. Glymour, and R. Scheines. Causation, prediction, and search. Springer-Verlag, New York, 1993.
29. P. Spirtes, C. Meek, and T.S. Richardson. An algorithm for causal inference in the presence of latent variables and selection bias. In C. Glymour and G.F. Cooper, editors, Computation, Causation and Discovery, pages 211-252. MIT Press, Cambridge, 1999.
30. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, Cambridge, second edition, 2000. With additional material by David Heckerman, Christopher Meek, Gregory F. Cooper and Thomas Richardson.
31. D.J. Stekhoven, I. Moraes, G. Sveinbjörnsson, L. Hennig, M.H. Maathuis, and P. Bühlmann. Causal stability ranking. Bioinformatics, 28:2819-2823, 2012.
32. S. van Dijk, L.C. van der Gaag, and D. Thierens. A skeleton-based approach to learning Bayesian networks from data. In Proceedings of the 7th Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-2003), pages 132-143. Springer, Berlin, 2003.
33. J. Zhang. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artif. Intell., 172:1873-1896, 2008.
34. X. Zhang, X.M. Zhao, K. He, L. Lu, Y. Cao, J. Liu, J.K. Hao, Z.P. Liu, and L. Chen. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics, 28:98-104, 2012.