I’m not sure what we are disagreeing about. In CDT you need causal Bayesian networks where the arrow orientation reflects physical causality. In EDT you just need probability distributions. You can represent them as Bayesian networks, but in this case arrow direction doesn’t matter, up to certain consistency constraints.
Why would EDT not having causal arrows be a problem?
I’m not sure what we are disagreeing about.
In CDT you need causal Bayesian networks where the arrow orientation reflects physical causality.
In EDT you just need probability distributions. You can represent them as Bayesian networks, but in this case arrow direction doesn’t matter, up to certain consistency constraints.
Why would EDT not having causal arrows be a problem?
Because the point of making decisions is to cause things to happen, and so encoding information about causality is a good idea.