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
Keywords: Causality discovery, graphical models, latent
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.