Evidential decision theory boggles my mind.

I have some sympathy for causal decision theory, especially when the causal description matches reality. But evidential decision theory is 100% bonkers.

The most common argument against evidential decision theory is that it does not care about the consequence of your action. It cares about correlation (broadly speaking), not causality, and acts as if both were same. This argument is sufficient to thoroughly discredit evidential decision theory, but philosophers keep giving it screen time.

Even if we lived in a world where correlation and causality were always the same (if that is possible), evidential decision theory would be wrong. Why? Because evidential decision theory requires distributions over actions and outcomes.

When you're acting in a decision problem, your action will often, or even usually, be unique. No one has every done that kind of action before. Consequently, there's no obvious distribution $p(a,x)$ over the action a and outcome x. But evidential decision theory requires such a distribution to function! Now you'll have to bootstrap your way to a distribution $p(a,x)$, flexing your philosophical creativity muscles. I suppose you could make this equal to $p(a,x)=1[x=x_a]$, the actual outcome when doing action a, at least when $x_a$ is deterministic. But why? You'll just introduce probabilities where none are needed.