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Two Algorithms for Inducing Structural Equation Models from Data

  • Paul R. Cohen
  • Dawn E. Gregory
  • Lisa Ballesteros
  • Robert St. Amant
Part of the Lecture Notes in Statistics book series (LNS, volume 112)

Abstract

We present two algorithms for inducing structural equation models from data. Assuming no latent variables, these models have a causal interpretation and their parameters may be estimated by linear multiple regression. Our algorithms are comparable with PC [Spirtes93] and IC [Pearl91a, Pearl91b], which rely on conditional independence. We present the algorithms and empirical comparisons with PC and IC.

Keywords

Latent Variable Structural Equation Model Causal Model Conditional Independence Target Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1996

Authors and Affiliations

  • Paul R. Cohen
    • 1
  • Dawn E. Gregory
    • 1
  • Lisa Ballesteros
    • 1
  • Robert St. Amant
    • 1
  1. 1.Computer Science Department, LGRCUniversity of MassachusettsAmherstUSA

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