The problem is that this cannot be made rigourous without making it into CDT.

I will use the smoking example because it is essential that you do not know which set you are in, which does not hold in the driving example. EDT tells you to calculate the probability that you will die if you do smoke and the probability that you will die if you do not smoke. These numbers can be compared. It's true that any evidence as to which gene you have would reverse this decision, but this is not part of any general process. We could adopt a general rule of dividing up the possibilities in this way with respect to every variable. However, one such variable is what choice you will make. Just as you can make set(people with gene) and set(people without gene), you could make set(people with gene who chose to smoke), set(people with gene who chose not to smoke), set(people without gene who chose to smoke), and set(people without gene who chose not to smoke). At this point, we have recovered CDT.

This was, as may be evident, unfinished. I intended to save it in drafts, and will rerelease it when it is actually written.

So what you're basically saying is that EDT is vulnerable to Simpson's Paradox?

But then, aren't all conclusions drawn from incomplete sets of data potentially at risk from unobserved causations? And complete sets of data are ridiculously hard (if not impossible) to obtain anyway.