Check if the current Causing implementation 2.x can handle cyclic models.
These ideas are imported from:
Cyclic example model:
- Y1 <- Y2
- Y2 <- Y1
Effect of Y2 on Y1: As in the acyclic case, simply intervene on Y2 and compute Y1_new:
effect = Y1_new - Y1_old
So do not cyclicly converge.
Check if the current Causing implementation 2.x can handle cyclic models.
These ideas are imported from:
I reviewed the literature on causal effects in cyclic systems. See Pearl (2011), p. 5 "On the Statistical Interpretation of Structural Equations". 2011 Pearl On the Statistical Interpretation of Structural Equations.pdf
This will apply for node and edge in our IME graphs:
Cyclic example model:
Effect of Y2 on Y1: As in the acyclic case, simply intervene on Y2 and compute Y1_new:
effect = Y1_new - Y1_old
So do not cyclicly converge.
"Beispiel 6 Zyklisches System" from the PDF documentation. I am not sure whether this example is implemented in Causing, but once it was.