Building Latent Variable Models'

Abstract

Researchers routinely face the problem of inferring causal relationships from large amounts of data, sometimes involving hundreds of variables. Often, it is the causal relationships between "latent" (unmeasured) variables that are of primary interest. The problem is how causal relationships between unmeasured variables can be inferred from measured data. For example, naval manpower researchers have been asked to infer the causal relations among psychological traits such as job satisfaction and job challenge from a data base in which neither trait is measured directly, but in which answers to interview questions are plausibly associated with each trait. By combining background knowledge with an algorithm that searches for causal structure among the unobserved variables, we have created a tool that can reliably extract useful causal information about latent variables from large data bases. In what follows we describe the class of causal models to which our..

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 92,150

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
2010-12-22

Downloads
41 (#389,665)

6 months
9 (#313,570)

Historical graph of downloads
How can I increase my downloads?

Author's Profile

Peter Spirtes
Carnegie Mellon University

Citations of this work

No citations found.

Add more citations

References found in this work

No references found.

Add more references