There is obviously a difference between observational data and experiments.
Yes! The difference is that experiments involve intervention. I thought the necessity of formalizing the notion of intervention is precisely what was under dispute here.
Well, kinda. I am not sure whether the final output -- the joint densities of outcomes -- will be different in a causal model compared to a properly specified conventional model.
To continue with the same example, it suffers from the expression "wet grass" meaning two different things -- either "I see wet grass" or "I made grass wet". This is your difference between just (a=1) and do(a=1) -- but conventional non-causal modeling doesn't have huge problems with this, it is fully aware of the difference.
And I don't know if it's necessary to formalize intervention. I freely concede that it's useful in certain areas but not so sure that's true for all areas.
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.