Abstract
Structural equation (SE) models provide a statistical model summarizing a multivariate probability distribution in terms of linear equations. When researchers interpret a SE model in terms of causal relationships, the SE model serves as a causal model. Researchers also give causal interpretations to models not currently expressible as SE models. Recognizing that both sets of models have expanding membership, the present chapter focuses primarily on the intersection of these two sets: causally interpreted SE models.
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Markus, K. (2004). Varieties of Causal Modeling: How Optimal Research Design Varies by Explanatory Strategy. In: van Montfort, K., Oud, J., Satorra, A. (eds) Recent Developments on Structural Equation Models. Mathematical Modelling: Theory and Applications, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-1958-6_10
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DOI: https://doi.org/10.1007/978-1-4020-1958-6_10
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