CAUSAL INFERENCE MODELSattempting to get at experience variables (e.g.,
exposure to discrimination) by using simple objec-
tive indicators such as race, sex, or age. Further-
more, one’s substantive models may involve feed-
back relationships so that simultaneous equation
systems must be joined to one’s measurement-
error models.
In all such instances, there will undoubtedly
be numerous specification errors in one’s models,
so that it becomes necessary to evaluate alternative
models in terms of their goodness of fit to the data.
Simple path-analytic methods, although heuristi-
cally helpful, will no longer be adequate. Fortu-
nately, there are several highly sophisticated com-
puter programs, such as LISREL, that enable social
scientists to carry out sophisticated data analyses
designed to evaluate these more complex models
and to estimate their parameters once it has been
decided that the fit to reality is reasonably close.
(See Joreskog and Sorbom 1981; Long 1983; and
Herting 1985.)
In closing, what needs to be stressed is that
causal modeling tools are highly flexible. They
may be modified to handle additional complica-
tions such as interactions and nonlinearities. Caus-
al modeling in terms of attribute data has been
given a firm theoretical underpinning by Suppes
(1970), and even ordinal data may be used in an
exploratory fashion, provided that one is willing to
assume that dichotomization or categorization has
not introduced substantial measurement errors
that cannot be modeled.
Like all other approaches, however, causal
modeling is heavily dependent on the assumptions
one is willing to make. Such assumptions need to
be made as explicit as possible—a procedure that
is unfortunately often not taken sufficiently seri-
ously in the empirical literature. In short, this set
of tools, properly used, has been designed to
provide precise meaning to the assertion that nei-
ther theory nor data can stand alone and that any
interpretations of research findings one wishes to
provide must inevitably also be based on a set of
assumptions, many of which cannot be tested with
the data in hand.
Finally, it should be stressed that causal mod-
eling can be very useful in the process of theory
construction, even in instances where many of the
variables contained in the model will remain
unmeasured in any given study. It is certainly a
mistake to throw out portions of one’s theory
merely because data to test it are not currently
available. Indeed, without a theory as to how miss-
ing variables are assumed to operate, it will be
impossible to justify one’s assumptions regarding
the behavior of disturbance terms that will contain
such variables, whether explicitly recognized or
not. Causal modeling may thus be an important
tool for guiding future research and for providing
guidelines as to what kinds of neglected variables
need to be measured.(SEE ALSO: Correlation and Regression Analysis; Episte-
mology; Multiple Indicator Models; Scientific Explana-
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