Title

Discussion of "learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables"

Document Type

Conference paper

Source Publication

Journal of Machine Learning Research

Publication Date

4-11-2011

Volume

15

First Page

16

Last Page

18

Abstract

Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables is discussed. The problem of inferring the presence of latent variables, their relation to the observables, and the relation among themselves, is considered. A different approach for identifying causal structures, one that results in much simpler equivalence classes, is provided. It is found that the computational cost is much higher than the procedure implemented, but if datasets are individually of modest dimensionality, it might be doable in practice. From the point of view of search algorithms for optimizing structure, much of the machinery of combinatorial optimization could optimize the penalized composite likelihood score by enforcing constraints such that the independence models over different subsets of variables agree on the overlapping sets.

Print ISSN

15324435

Publisher Statement

Copyright © 2011 Proceedings of Machine Learning Research. Access to external full text or publisher's version may require subscription.

Full-text Version

Publisher’s Version

Language

English

Recommended Citation

Zhang, J., & Silva, R. (2011). Discussion of "learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables". In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 11-13 April 2011, Fort Lauderdale, FL, USA (pp.16-18). Retrieved from http://proceedings.mlr.press/v15/zhang11a.html

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