Two Algorithms for Inducing Structural Equation Models from Data

Paul R. Cohen, Dawn E. Gregory, Lisa Ballesteros, Robert St. Amant
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:129-139, 1995.

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 [15] and IC [12,11], which rely on conditional independence. We present the algorithms and empirical comparisons with $\mathrm{PC}$ and IC.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-cohen95a, title = {Two Algorithms for Inducing Structural Equation Models from Data}, author = {Cohen, Paul R. and Gregory, Dawn E. and Ballesteros, Lisa and Amant, Robert St.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {129--139}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/cohen95a/cohen95a.pdf}, url = {https://proceedings.mlr.press/r0/cohen95a.html}, 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 [15] and IC [12,11], which rely on conditional independence. We present the algorithms and empirical comparisons with $\mathrm{PC}$ and IC.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Two Algorithms for Inducing Structural Equation Models from Data %A Paul R. Cohen %A Dawn E. Gregory %A Lisa Ballesteros %A Robert St. Amant %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-cohen95a %I PMLR %P 129--139 %U https://proceedings.mlr.press/r0/cohen95a.html %V R0 %X 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 [15] and IC [12,11], which rely on conditional independence. We present the algorithms and empirical comparisons with $\mathrm{PC}$ and IC. %Z Reissued by PMLR on 01 May 2022.
APA
Cohen, P.R., Gregory, D.E., Ballesteros, L. & Amant, R.S.. (1995). Two Algorithms for Inducing Structural Equation Models from Data. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:129-139 Available from https://proceedings.mlr.press/r0/cohen95a.html. Reissued by PMLR on 01 May 2022.

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