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Finding latent variable models in large databases
Author(s) -
Scheines Richard,
Spirtes Peter
Publication year - 1992
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550070704
Subject(s) - latent variable , computer science , variable (mathematics) , database , latent variable model , data mining , artificial intelligence , mathematics , mathematical analysis
Structural equation models with latent variables are used widely in psychometrics, econometrics, and sociology to explore the causal relations among latent variables. Since such models often involve dozens of variables, the number of theoretically feasible alternatives can be astronomical. Without computational aids with which to search such a space, researchers can only explore a handful of alternative models. We describe a procedure that can find information about the causal structure among latent, or unmeasured variables. the procedure is asymptotically reliable, feasible on data sets with as many as a hundred variables, and has already proved useful in modeling an empirical data set collected by the U.S. Navy. © 1992 John Wiley & Sons, Inc.