SEM analyses are popular in the social sciences because these analytic techniques help us break down complex concepts and understand causal processes, but the complexity of the models can introduce substantial variability in the results depending on the presence or absence of conventional control variables, the sample size, and the variables of interest. The use of experimental designs may address some of these doubts.

Today, SEM forms part of a basis of machine learning and (interpretable) neural networks.