Reading Math: Pearl, Causal Bayes Nets, and Functional Causal Models
Hi all, I just started a doctoral program in psychology, and my research interest concerns causal reasoning. Since Pearl's Causality, the popularity of causal Bayes nets as psychological models for causal reasoning has really grown. Initially, I had some serious reservations, but now I'm beginning to think a great many...
Thanks, that is helpful.
My claim was that, if we simply represent the gears example by representing the underlying (classical) physics of the system via Pearl's functional causal models, there's nothing cyclic about the system. Thus, Pearl's causal theory doesn't need to resort to the messy expensive stuff for such systems. It only needs to get messy in systems which are a) cyclic, and b) implausible to model via their physics-- for example, negative and positive feedback loops (smoking causes cancer causes despair causes smoking).