[phkahler]

>> Can you cite any parts of the article that support your view on this?

How about this: "In the interview, Pearl dismisses most of what we do in ML as curve fitting. While I believe that's an overstatement (conveniently ignores RL for example), it's a nice reminder that most productive debates are often triggered by controversial or outright arrogant comments. Calling machine learning alchemy was a great recent example."

When a person is dismissive of an entire field and claims to have a better way, that often comes off as arrogant (even if it is true). My interpretation is "harsh" while the author uses the word "overstatement". You'll also see "arrogant" in there and that last line calling it "alchemy" really has to be interpreted with negative connotations. Perhaps I read more into it than was written, but that was the impression I got.

[mlthoughts2018]

Though the authors mentions that one comment of Pearl, all of the causal inference / graphical model work takes the opposite stance.

The popular academic writing in that field claims everyone else is being arrogant. It’s not a statement that Pearl is arrogant for dismissing huge chunks if ML, rather that since causal inference is such a cure-all, then everyone else is arrogant for not dropping everything to use it everywhere.

There’s no spirit i this article of saying, “boy it looked like a short-sighted criticism of ML, but now that I look at it, the causal inference people are right after all, and ML people are wrong.”

It may try to disingenuously frame it that way, but this is not what they are saying.