No, this is not Simpson's paradox. Or rather, the reason Simpson's paradox is counterintuitive is precisely the same reason that you should not use conditional probabilities to represent causal effects. That reason is "confounders," that is common causes of both your conditioned on variable and your outcome.

Simpson's paradox is a situation where:

P(E|C) > P(E|not C), but

P(E|C,F) < P(E|not C,F) and P(E|C,not F) < P(E|not C, not F).

If instead of conditioning on C, we use "arbitrary decisions" or do(.), we get that if

P(E|do(C),F) < P(E|do(not C),F), and P(E|do(C),not F) < P(E|do(not C), not F), then

P(E|do(C)) < P(E|do(not C))

which is the intuitive conclusion people like. The issue is that the first set of equations is a perfectly consistent set of constraints on a joint density P(E,C,F). However, people want to interpret that set of constraints causally, e.g. as a guide for making a decision on C. But decisions are not evidence, decisions are do(.). Hence you need the second set of equations, which do have the property of "conserving inequalities by averaging" we want.

See also: http://bayes.cs.ucla.edu/R264.pdf

In my example, the issue was that p(Y|do(A)) was different from p(Y|A) due to the confounding effect of "health status" C. The point is that interventions remove the influence of confounding common causes by "being arbitrary" and not depending on them.

EDT, and more generally standard probability theory, simply fails on causality due to a lack of explicit axiomatization of causal notions like "confounder" or "effect."