In my understanding, we use directed acyclic graphs to model causality in Bayesian networks. But it is common sense that there are feedback loops in life. How do we deal with this?
- Reduce to strongly connected components ("feedback clusters"), abstract out the content interior to the clusters, and model the interaction between the clusters, using Bayes net techniques, do-calculus, path coefficients, etc.?
- Use techniques adapted to non-acyclic graphs?
The correct answer to my question might be something like
There is no "how 'we' deal with this", at least not yet. Causal modeling is still developing, and there is no one good answer to this question. People have tried different modifications for different objectives, such as mixing undirected and directed graphs, or converting to an undirected moral graph.
Still, I'm surprised I haven't encountered any discussion of the matter as I'm reading the expository articles on the subject.
I'm interested in discussion of the principle (philosophy-adjacent), or discussion of the practice (math/stats).