When does it make sense to apply SEM to experimental designs? I've often seen it mentioned that while SEM is generally used in relation to non-experimental designs it can be used in relation to experimental designs too. For example, Kline (2016) writes that 

Most applications of SEM are in nonexperimental designs, but data from
  experimental or quasi-experimental designs can be analyzed, too. (p. 10)

I understand that a major point of SEM is to in some sense disconfirm causal hypotheses, and that with an experimental design one might be able to make causal inferences much more simply. 
However, I am wondering under what circumstances it does, and does not, make sense to apply SEM to experimental designs. Are there rules of thumb that can tell me when applying SEM to an experimental design might be more or less advantageous?
Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford publications.
 A: The advantages of SEM over regression are to model multiple outcomes and to model latent variables. If you have either of those in an experiment, then you should use SEM.
For example, consider a mediation analysis. You have multiple outcomes (the mediator and the final outcome), and you can model those relationships using a system of simultaneous equation models. Assuming you do work to remove confounding of the mediator-outcome relationship, you can use SEM to make a causal mediation claim and estimate the causal mediation effects. There are other ways to estimate such effects (e.g., using conditional process or causal mediation techniques), but SEM is a straightforward method that follows directly from the research question.
Consider also an experiment with a latent outcome measured with a scale. You can use SEM to model the relationship between the experimental manipulation and the latent variable and between the latent variable and the indicators. You can then make a causal claim about the effect of the manipulation of the latent variable. SEM is the only framework that allows you to perform such an analysis validly.
Remember that SEM is not just about assessing causal hypotheses but also estimating specific effects. In experiments, one is often interested in estimating specific effects, and SEM provides a convenient and general framework for doing so.
