Well, since p(rain | grass wet) is high, it seems making the grass wet via a garden hose will make rain more likely. Of course you might say that "making the grass wet" and "seeing the grass wet" is not the same thing, in which case I agree!
The fact that these are not the same thing is why people say conditioning and interventions are not the same thing.
You can of course say that you can still use the language of conditional probability to talk about "doing events" vs "seeing events." But then you are just reinventing interventions (as will become apparent if you try to figure out axioms for your notation).
Well, since p(rain | grass wet) is high, it seems making the grass wet via a garden hose will make rain more likely.
That's a strawman. The conditional probability we're talking about has a clear (if explicitly unstated) temporal ordering: P(rain in the past | wet grass in the present).
But then you are just reinventing interventions
Talking about conditional probability was widespread long before people started talking about interventions.
It seems to me that the language of interventions, etc. is just a formalism that is convenient for certain types of analysis, but I'm not seeing that it means anything new.
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.