I see. So then if I were to give you a causal decision problem, can you tell me what the right answer is using only a prediction engine? I have a list of them right here!
The general form of these problems is : "We have a causal model where an outcome is death. We only have observational data obtained from this causal model. We are interested in whether a given intervention will reduce the death rate. Should we do the intervention?"
Observational data is enough for the predictor, right? (But the predictor doesn't get to see what the causal model is, after all, it just works on observational data and is agnostic of how it came about).
So then if I were to give you a causal decision problem, can you tell me what the right answer is using only a prediction engine?
A good enough prediction engine, yes.
We only have observational data obtained from this causal model.
Huh? You don't obtain observational data from a model, you obtain it from reality.
Observational data is enough for the predictor, right?
That depends. I think I understand prediction models wider than you do. A prediction model can use any kind of input it likes if it finds it useful.
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.