Accession Number : ADA557445
Title : The Causal Foundations of Structural Equation Modeling
Descriptive Note : Technical rept.
Corporate Author : CALIFORNIA UNIV LOS ANGELES DEPT OF COMPUTER SCIENCE
Personal Author(s) : Pearl, Judea
Report Date : 16 Feb 2012
Pagination or Media Count : 37
Abstract : The role of causality in SEM research is widely perceived to be, on the one hand, of pivotal methodological importance and, on the other hand, confusing, enigmatic and controversial. The confusion is vividly portrayed, for example, in the influential report of Wilkinson and Task Force s (1999) on Statistical Methods in Psychology Journals: Guidelines and Explanations. In discussing SEM, the report starts with the usual warning: It is sometimes thought that correlation does not prove causation but causal modeling does. [Wrong! There are] dangers in this practice. But then ends with a startling conclusion: The use of complicated causal-modeling software [read SEM] rarely yields any results that have any interpretation as causal effects. The implication being that the entire enterprise of causal modeling, from Sewell Wright (1921) to Blalock (1964) and Duncan (1975), the entire literature in econometric research, including modern advances in graphical and nonparametric structural models have all been misguided, for they have been chasing parameters that have no causal interpretation.
Descriptors : *EQUATIONS , GRAPHICS , LOGIC , MODELS
Subject Categories : Numerical Mathematics
Distribution Statement : APPROVED FOR PUBLIC RELEASE