期刊
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
卷 102, 期 -, 页码 74-85出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2018.08.002
关键词
Hybrid causal structure learning; Maximal ancestral graphs; Max-Min Hill Climbing; MMPC
资金
- European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC [617393]
We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max-Min Hill-Climbing ((MHC)-H-3) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph. (MHC)-H-3 builds upon a previously proposed method, namely GSMAG, by introducing a constraint-based first phase that greatly reduces the space of structures to investigate. On a large scale experimentation we show that the proposed algorithm greatly improves on GSMAG in all comparisons, and over a set of known networks from the literature it compares positively against FCI and cFCI as well as competitively against GFCI, three well known constraint-based approaches for causal network reconstruction in presence of latent confounders. (C) 2018 Published by Elsevier Inc.
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