Simulate a lot of patients
Simulations (and computer programs in general -- think about how debuggers for computer programs work) are causal models, not purely predictive models. Your answer does no work, because being able to simulate at that level of fidelity means we are already Done with the science of what we are simulating. In particular our simulator will contain in it a very detailed causal model that would contain answers to everything we might want to know. The question is what do we do when our information isn't very good, not when we can just say "let's ask God."
This is a quote from an ML researcher today, who is talking about what is done today. And what is done today for purely predictive modeling are those crazy deep learning networks or support vector machines they have in ML. Those are algorithms specifically tailored to answering p(Y | X) kinds of questions (e.g. prediction questions), not causal questions.
edit: to add to this a little more. I think there is a general mathematical principle at play here, which is similar in spirit to Occam's razor. This principle is : "try to use the weakest assumptions needed to get the right answer." It is this principle that makes "Omega-style simulations" an unsatisfactory answer. It's a kind of overfitting of the entire scientific process.
A good enough prediction engine can substitute, to a degree, for a causal model. Obviously, not always and once you get outside of its competency domain it will break, but still -- if you can forecast very well what effects will an intervention produce, your need for a causal model is diminished.
Yann LeCun, now of Facebook, was interviewed by The Register. It is interesting that his view of AI is apparently that of a prediction tool:
"In some ways you could say intelligence is all about prediction," he explained. "What you can identify in intelligence is it can predict what is going to happen in the world with more accuracy and more time horizon than others."
rather than of a world optimizer. This is not very surprising, given his background in handwriting and image recognition. This "AI as intelligence augmentation" view appears to be prevalent among the AI researchers in general.