Graphs, Causality, and Structural Equation Models

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Abstract
Structural equation models (SEMs) have dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational answers. Recent developments in the areas of graphical models and the logic of causality show potential for alleviating such difficulties and, thus, revitalizing structural equations as the primary language of causal modeling. This article summarizes several of these developments, including the prediction of vanishing partial correlations, model testing, model equivalence, parametric and nonparametric identifiability, control of confounding, and covariate selection. These developments clarify the causal and statistical components of SEMs and the role of SEM in the empirical sciences.