Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University


New D-Separation Identification Results for
Learning Continuous Latent Variable Models

Ricardo Silva, Richard Scheines

July 2005


Keywords: Causality discovery, graphical models, latent variable models

Learning the structure of graphical models is an important task, but one of considerable di culty when latent variables are involved. Because conditional independences using hidden variables cannot be directly observed, one has to rely on alternative methods to identify the d-separations that define the graphical structure. This paper describes new distribution-free techniques for identifying d-separations in continuous latent variable models when non-linear dependencies are allowed among hidden variables.

24 pages

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