I haven't seen any "two-way" causation assumptions before. Can you elaborate. Statistical models are directionally agnostic. They don't know if it is Y = X or X = Y.

I will post a graphic tomorrow, but it is like I am trying to find predictors of a mediator using its parents and children. There is a thing called Markovian blanket, meaning all you need to know about a variable is its causes and effects, anything further upstream or downstream than the term is conditionally independent of it given you control for the blanket variables. However, there is some incest in my model, since the parent term is a direct cause of the target variable and its child, but it is also mediated. If this hasn't been written up, that would be a pseudo-interesting term to use, though slightly incorrect (since direct and indirect effect occur from the parent).

X -> Y -> Z <- X, want to model Y, or find variables associated with it, no loops in this. Another way to think about it is that I am trying to model the targeted variable which it's relationship with Z is confounded.