Structural Equations and Causal Explanations: Some Challenges for Causal SEM
One common application of structural equation modeling (SEM) involves expressing and empirically
investigating causal explanations. Nonetheless, several aspects of causal explanation that have
an impact on behavioral science methodology remain poorly understood. It remains unclear whether
applications of SEM should attempt to provide complete explanations or partial explanations.
Moreover, it remains unclear what sorts of things researchers can best take as causes and effects.
Finally, the meaning of causal assertions itself remains poorly understood. Attempting to clarify
the use of structural equations as causal explanations by addressing these issues has implications
for behavioral science methodology because applications of SEM typically remain vague about
causation and thus about their substantive conclusions. Research aimed at clarifying these issues
can lead to a sharper and more refined use of SEM for causal explanation, and by extension, clarify
behavioral science methodology more generally.
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