Structural equation models (SEA ls) have dominated causal analysis in
the social and behavioral sciences since the 1960s. Currently, many SE
M practitioners are having difficulty articulating the causal content
of SEM and an seeking foundational answers. Recent developments in the
areas of graphical models and the logic of causality show potential f
or alleviating such difficulties and thus, revitalizing structural equ
ations as the primary language of causal modeling. This article summar
izes several of these developments, including the prediction of vanish
ing partial correlations, model testing, model equivalence, parametric
and nonparametric identifiability, control of confounding, and covari
ate selection These developments clarify the causal and statistical co
mponents of SEMs and the role of SEM in the empirical sciences.