Emphasizing causation as a functional relationship between
variables that describe objects, Linear Causal Modeling with
Structural Equations integrates a general philosophical theory of
causation with structural equation modeling (SEM) that concerns the
special case of linear causal relations. In addition to describing
how the functional relation concept may be generalized to treat
probabilistic causation, the book reviews historical treatments of
causation and explores recent developments in experimental
psychology on studies of the perception of causation. It looks at
how to perceive causal relations directly by perceiving quantities
in magnitudes and motions of causes that are conserved in the
effects of causal exchanges.
The author surveys the basic concepts of graph theory useful in
the formulation of structural models. Focusing on SEM, he shows how
to write a set of structural equations corresponding to the path
diagram, describes two ways of computing variances and covariances
of variables in a structural equation model, and introduces matrix
equations for the general structural equation model. The text then
discusses the problem of identifying a model, parameter estimation,
issues involved in designing structural equation models, the
application of confirmatory factor analysis, equivalent models, the
use of instrumental variables to resolve issues of causal direction
and mediated causation, longitudinal modeling, and nonrecursive
models with loops. It also evaluates models on several dimensions
and examines the polychoric and polyserial correlation coefficients
and their derivation.
Covering the fundamentals of algebra and the history of
causality, this book provides a solid understanding of causation,
linear causal modeling, and SEM. It takes readers through the
process of identifying, estimating, analyzing, and evaluating a
range of models.
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