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Comment by bachmeier | Hacker News Reader

Oh, I'm completely on board with being explicit about any assumptions you're making, and for thinking deeply about the causal relations, and for thinking about the reliability of the analysis. I've been critical of economists for not thinking things through. And I'm definitely on board with teaching using toy problems.

The problem is that, as far as I can tell, Pearl doesn't go beyond making points with toy problems. He hasn't done much empirical work (has he published a single empirical paper?) or even read much of the empirical literature he's criticizing as worthless. Ultimately, the question is whether policy and other decisions will be better using a particular framework. The fact that Pearl writes with the aggressive confidence of a Hacker News commenter does not mean he's right.

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But from a mathematical point of view this insistence of focusing on empirical work is a little bit disheartening isn’t it?

And frankly Judea Pearl is hardly a random internet tinfoil hat guy, nor is he asking people to invest a massive effort checking his looong work (as we eg often see with random NP-completeness “proofs”, or was discussed with the time investment needed to check Mochizuki’s ABC proof - no, he just asks Gellman to apply his own familiar techniques to a toy problem. It does not sound unreasonable at all.

> But from a mathematical point of view this insistence of focusing on empirical work is a little bit disheartening isn’t it?

Why? The only thing that matters is the quality of the empirical work. You can get caught up in philosophical debates about the best way to do research. If it has no impact on empirical work, it's useless.

That's not to say Pearl's arguments are wrong or that his work is useless. The problem is the incompleteness of his arguments. You can't arrogantly dismiss empirical work just because it's imperfect. There's no reason a priori to expect that Pearl's approach will lead to better decisions.

There are many self-proclaimed experts who can point out the flaws in programming language designs, but that doesn't mean they can design a better language, and it doesn't mean existing programming languages are useless. Pearl's approach is not some kind of magic pixie dust that suddenly guarantees your empirical work is more trustworthy. It's unfortunate that Pearl thinks it is, and it prevents him from having a reasonable conversation about the topic.

Well from my point of view when I read the link, I see (with a little bit of characterization) two academics, one of them points at an issue on the standard statistical approach and tools, provides a reproduceable way to showcase that answer, and the other one answers “not interesting - i’ll go now to do real work”. And I found that avoidance of a discussion of a shared example disheartening.

And Pearl isnt just complaining - the provided an alternative. So it seems that one side identified a problem, curated examples, delivered a solution, and now the other side doesnt care about the problem - and causality is not a trivial problem. As academic discussion, not a business meeting, to me thats subpar.

That’s only my view from the outside, it may well be the case that it is wrong and I just need to review more in depth Pearl to see that he does not bring anything useful.

They've been debating this for many years, and Gelman is a coauthor and student of Rubin, who has been debating with Pearl for decades. Gelman is saying he doesn't want to debate the same issues yet again.

The primary message of Pearl is "all other methods are trash and all empirical work done using those methods is trash." My interpretation of the post is that Gelman is arguing other methods are not trash.

I probably agree more with Pearl than Gelman on the details, but Pearl's approach is just not appropriate for an academic setting.

What they are really fighting about is who gets to reference colloquial notions of causality when discussing their work. I don't think it's fair to say that Pearl treats the rest of stats as trash, but he is saying that they are misleading folks by describing their inference as evidence of cause and effect - which is about as bad for most academics and especially for the Rubin crew who have often policed integrity of other inference regimes (like less rigorous ML).

Debating who gets to use which words is a great way to make sure your debate only matters to other academics. I'd love to see the causality camp make their point by unlocking some great new applied results instead!

Refusing to provide a demonstration of the statistical technique on a toy example constructed for pedagogic purpose, hardly constitutes as a "debate". I would file that under: using a lot of words to avoid answering a direct and fundamental question

http://causality.cs.ucla.edu/blog/index.php/2016/02/01/works...

Is this sufficiently applied for you? Pearl may not apply his work on causality often, but there are a lot of people who are on the applied side who do.

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