[nyssos]

> How to know if model 1 or model 2 is more "real" or, for lack of a better term, more useful and reflective of reality?

You don't. Given observational data alone, it's typically only possible to determine which d-separation equivalence class you're in. Identifying the exact causal structure requires intervening experimentally.

> There should be some way to determine a model's likelihood of structure

Why? If the information isn't there, it isn't there. No technique can change that.

[bangkoksbest]

Acyclic structure on variables is a very strong pre-supposition that, honestly, is not how many systems in engineering are well-described by, so I don't like this idea of boiling causality solely down to DAG-dependent phrases like "d-separation" or "exact causal structure". Exact causal structure a.k.a. actual causality is particular to one experimental run on one intervention.

[nyssos]

D-separation still works for cyclic graphs, it just can't rule out causal relationships between variables that lie on the same cycle. And neither can any other functional-form-agnostic method, because in general feedback loops really do couple everything to everything else.

More rigorously: given a graph G for a structural equation model S, construct a DAG G' as follows

- Find a minimal subgraph C_i transitively closed under cycle membership (so a cycle, all the cycles it intersects, all the cycles they intersect, and so on)

- Replace each C_i with a complete graph C'_i on the same number of vertices, preserving outgoing edges.

- Add edges from the parents of any vertices in C_i (if not in C_i themselves) to all vertices in C'_i

- Repeat until acyclic

d-separation in G' then entails independence in S given reasonable smoothness assumptions I don't remember the details of off the top of my head.