My take is that there is no such thing as causation. Correlation is all there is ...

I keep having to link this:

http://www.smbc-comics.com/index.php?db=comics&id=1994

In other words at the expense of potentially having every Ei with i <* j affect the probability of E_i I can have any causal order I want on the events and get the same results.

Causal models have to do with interventions not with node orders in a Bayesian network. A causal model is not the same thing as a Bayesian network (which Eliezer got wrong in his post, and has yet to fix, by the way). Causal models are not about making better predictions, they are about cause effect relationships (causal effects, mediation analysis, confounders, things like that). I think reading standard stuff on interventionist causality might be a good idea: Pearl's Causality book or the CMU book (Causation, Prediction and Search).