Eric Brown asks:

How does Stan and its Bayesian modeling relate to structural equation modeling? Do you know of a resource that attempts to explain the concepts behind SEM in terms of Stan nomenclature and concepts?

Some research that I’ve looked into uses SEM to evaluate latent factors underlying multiple measurements with associated errors; or use SEM to relate different measurements of the same physical property. I have a hard time wrapping my head around that analysis and would prefer to use what I know (Stan) and investigate the same issues.

Any suggestions?

My reply:

There are two aspects to a structural equation model: the statistical model and the causal interpretation.

The statistical model is a big multivariate distribution, and there should be no problem fitting it in Stan. I haven’t personally fit such models myself, but my guess is that if you put a query on the Stan Discourse list, asking if anyone’s fit a structural equation model in Stan, that you’ll get some responses.

The causal interpretation is just a separate issue from the fitted model. I think the usual causal interpretations of structural equation models are typically over-ambitious: without making lots of assumptions, there’s a limit to how much causal knowledge you can get from observational data, and traditional structural equation modeling does not make a lot of formal assumptions. Fitting a structural equation model in Stan won’t solve this problem, because even if you put strong priors on the parameters in the model, this doesn’t give you priors on the causal inferences. From a statistical perspective, causal inference corresponds to predictions about potential outcomes, and structural equation models, as traditionally written, just model the data, they don’t model potential outcomes. Some of these concerns are discussed in the causal inference chapters of my book with Jennifer Hill. We don’t talk about structural equation models, but our general discussions of causal inference should be relevant to understanding these issues.

tl;dr: I think Stan’s an excellent way to fit a structural equation model, considering it as a probability model, a math problem to fit a model to data. To go causal (which is the usual purpose of structural equation modeling), you might not want to fit a structural equation model at all!

There’s one other thing, which is that these models can be so big, that often people try to simplify the model or estimate some underlying structure by using rules such as statistical significance or Bayes factors to remove links from the model. I generally don’t like this, the practice of trying to estimate causal structure based on data. I discuss this a bit in my 2011 paper, Causality and Statistical Learning: