4 ms·

> It almost feels to me like he is shopping around for some sexy application area where his one-upsmanship approach will catch on too give him a chance at the h

by closed 8y ago

> It almost feels to me like he is shopping around for some sexy application area where his one-upsmanship approach will catch on too give him a chance at the hype gravy train or something. This doesn't seem like a very fitting description of Pearl. In his work, he is very careful to cite existing approaches (structural equation model literature, various topics from graphical models). In his various discussions with Gelman, he comes off as freakishly polite and not looking to one up.

mlthoughts2018 8y ago
I'm sorry but I simply don't agree about the politeness comment. As linked from a Quora post that goes into, this was one of Pearl's original statements about the disagreement (link to the original at the UCLA site appears to have been taken down) [0]: > "I therefore invite my colleagues... to familiarize themselves with the miracles of do-calculus. Take any causal problem for which you know the answer in advance, submit it for analysis through the do-calculus and marvel with us at the power of the calculus to deliver the correct result in just 3–4 lines of derivation. Alternatively, if we cannot agree on the correct answer, let us simulate it on a computer, using a well specified data-generating model, then marvel at the way do-calculus, given only the graph, is able to predict the effects of (simulated) interventions. I am confident that after such experience all hesitations will turn into endorsements. BTW, I have offered this exercise repeatedly to colleagues from the potential outcome camp, and the response was uniform: “we do not work on toy problems, we work on real-life problems.” Perhaps this note would entice them to join us, mortals, and try a small problem once, just for sport." This is absolutely the cheeky spirit of one-upsmanship I am talking about. The offers are always framed in terms of "look how causal inference supersedes everything," which is not a charitable take on approaches from others, especially in historical applied ML, that might have already developed some of the same underlying ideas. [0]: https://www.quora.com/Why-is-there-a-dispute-between-Judea-Pearl-and-Rubin-with-respect-to-the-theoretical-frameworks-used-in-causal-modelling https://www.quora.com/Why-is-there-a-dispute-between-Judea-P...
- closed 8y ago
  I don't know. The issue he is addressing in your quote is that people often leverage criticisms of his approach that are just verbal statements. Pearl wants people to use data-generating models to make their concerns explicit. The link you used explains the situation pretty well. If anything Pearl's regular acknowledgement of graphical models seems to be an indication that he is mindful of at least one very common approach in current ML.
  - fjsolwmv 8y ago
    Isn't it incumbent on Pearl or someone on the do-calculus school to run an experiment to show it performs better than popular ML systems? It's a beautiful theory, but it hearkens back to the symbolic AI era that has had limited effectiveness.
    - closed 8y ago
      In theory, yes. However, I think in practice addressing the concerns of critics is often out of Pearl's hands. Until they supply a "ground truth" or data generating model, he has a dilemma: * if he doesn't create a data generating model, then arguments for / against his approach will be specious. * if he creates a data generating model, they can claim it doesn't reflect reality. In the case of Judea Pearl and Andy Gelman, it seems like the point of contention is much broader than the do-calculus. Andy Gelman does not seem to be a fan of structural equation modeling / similar graphical models.
      - mlthoughts2018 8y ago
        How is it out of Pearl’s hands? Also, Gelman & Rubin already did look into Pearl’s models, and even agreed that for some toy model examples, the technique works as intended, but that there are serious how-things-work-in-practice reasons why Pearl’s models are unlikely to be mathematically appropriate for some real world use cases. It’s really a fair response from them to Pearl, especially when the whole time Pearl is presenting it like causal inference is a miracle cure-all. All I am seeing in your comments is hand waving attempts to shift the burden of proof onto the group of practitioners who already looked into this stuff and weren’t convinced! So why does it being incumbent on Pearl or on another causal inference practitioner to demonstrate it scaling up to a more complicated in-practice problem still get qualified with an “in theory” from you? Why isn’t it resoundingly obvious by this point that the burden of proof lies with Pearl, and that people would be happy to hear if he can use these models for large-scale, practical use cases, but they (rightfully) don’t see a reason (even after looking into the models) to spend their own time doing it?

by closed 8y ago

mlthoughts2018 8y ago

I'm sorry but I simply don't agree about the politeness comment. As linked from a Quora post that goes into, this was one of Pearl's original statements about the disagreement (link to the original at the UCLA site appears to have been taken down) [0]: > "I therefore invite my colleagues... to familiarize themselves with the miracles of do-calculus. Take any causal problem for which you know the answer in advance, submit it for analysis through the do-calculus and marvel with us at the power of the calculus to deliver the correct result in just 3–4 lines of derivation. Alternatively, if we cannot agree on the correct answer, let us simulate it on a computer, using a well specified data-generating model, then marvel at the way do-calculus, given only the graph, is able to predict the effects of (simulated) interventions. I am confident that after such experience all hesitations will turn into endorsements. BTW, I have offered this exercise repeatedly to colleagues from the potential outcome camp, and the response was uniform: “we do not work on toy problems, we work on real-life problems.” Perhaps this note would entice them to join us, mortals, and try a small problem once, just for sport." This is absolutely the cheeky spirit of one-upsmanship I am talking about. The offers are always framed in terms of "look how causal inference supersedes everything," which is not a charitable take on approaches from others, especially in historical applied ML, that might have already developed some of the same underlying ideas. [0]: https://www.quora.com/Why-is-there-a-dispute-between-Judea-Pearl-and-Rubin-with-respect-to-the-theoretical-frameworks-used-in-causal-modelling https://www.quora.com/Why-is-there-a-dispute-between-Judea-P...

closed 8y ago
I don't know. The issue he is addressing in your quote is that people often leverage criticisms of his approach that are just verbal statements. Pearl wants people to use data-generating models to make their concerns explicit. The link you used explains the situation pretty well. If anything Pearl's regular acknowledgement of graphical models seems to be an indication that he is mindful of at least one very common approach in current ML.
- fjsolwmv 8y ago
  Isn't it incumbent on Pearl or someone on the do-calculus school to run an experiment to show it performs better than popular ML systems? It's a beautiful theory, but it hearkens back to the symbolic AI era that has had limited effectiveness.
  - closed 8y ago
    In theory, yes. However, I think in practice addressing the concerns of critics is often out of Pearl's hands. Until they supply a "ground truth" or data generating model, he has a dilemma: * if he doesn't create a data generating model, then arguments for / against his approach will be specious. * if he creates a data generating model, they can claim it doesn't reflect reality. In the case of Judea Pearl and Andy Gelman, it seems like the point of contention is much broader than the do-calculus. Andy Gelman does not seem to be a fan of structural equation modeling / similar graphical models.
    - mlthoughts2018 8y ago
      How is it out of Pearl’s hands? Also, Gelman & Rubin already did look into Pearl’s models, and even agreed that for some toy model examples, the technique works as intended, but that there are serious how-things-work-in-practice reasons why Pearl’s models are unlikely to be mathematically appropriate for some real world use cases. It’s really a fair response from them to Pearl, especially when the whole time Pearl is presenting it like causal inference is a miracle cure-all. All I am seeing in your comments is hand waving attempts to shift the burden of proof onto the group of practitioners who already looked into this stuff and weren’t convinced! So why does it being incumbent on Pearl or on another causal inference practitioner to demonstrate it scaling up to a more complicated in-practice problem still get qualified with an “in theory” from you? Why isn’t it resoundingly obvious by this point that the burden of proof lies with Pearl, and that people would be happy to hear if he can use these models for large-scale, practical use cases, but they (rightfully) don’t see a reason (even after looking into the models) to spend their own time doing it?