Huh? You don't obtain observational data from a model, you obtain it from reality.
Right, the data comes from the territory, but we assume the map is correct.
That depends. I think I understand prediction models wider than you do.
The point is, if your 'prediction model' has a rich enough language to incorporate the causal model, it's no longer purely a prediction model as everyone in the ML field understands it, because it can then also answer counterfactual questions. In particular, if your prediction model only uses the language of probability theory, it cannot incorporate any causal information because it cannot talk about counterfactuals.
So are you willing to take me up on my offer of solving causal problems with a prediction algorithm?
the data comes from the territory, but we assume the map is correct.
You don't need any assumptions about the model to get observational data. Well, you need some to recognize what are you looking at, but certainly you don't need to assume the correctness of a causal model.
no longer purely a prediction model as everyone in the ML field understands it
We may be having some terminology problems. Normally I call a "prediction model" anything that outputs testable forecasts about the future. Causal models are a subset of prediction models. Withi...
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.