Learning the structure of linear latent variable models

Abstract

We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems. Keywords: latent variable models, causality, graphical models..

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 92,038

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

  • Only published works are available at libraries.

Analytics

Added to PP
2009-01-28

Downloads
96 (#179,967)

6 months
6 (#522,810)

Historical graph of downloads
How can I increase my downloads?

Author's Profile

Peter Spirtes
Carnegie Mellon University

References found in this work

No references found.

Add more references