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For balance and discussion, Andrew Gelman's critical review of 'The Book of Why': https://statmodeling.stat.columbia.edu/2019/01/08/book-pearl-mackenzie/ https:

For balance and discussion, Andrew Gelman's critical review of 'The Book of Why': https://statmodeling.stat.columbia.edu/2019/01/08/book-pearl-mackenzie/ https://statmodeling.stat.columbia.edu/2019/01/08/book-pearl...

epistasis 6y ago
Thanks for sharing this! I knew that Gelman and Pearl have been going back and forth for years, and it's great to see this. I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction. That said, I think their disagreements don't have much effect on practice, and have as much to do with their different academic lineages as anything else. I expect that any dispute will be resolvable as the science progresses
- elcritch 6y ago
  > I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction. Are you able to elaborate?! It'd be really interesting to hear what/if tooling exists for these graphs. I just found the book recently and am listening to the Audible version. It also reaffirms my desire for a "Bayesian inference" spreadsheet like tool. Something that'd help organize a few dozen thoughts ideas for researchers/engineers.
  - currymj 6y ago
    R package "daggity" is excellent. http://www.dagitty.net/ http://www.dagitty.net/
  - epistasis 6y ago
    I've used libDAI in the past, but I'm not sure if it's maintained anymore. I've come across several other toolkits that looked interesting since then, but never had the right project. There's a group at (CMU?)/Pitt that I think now has a Python package for causal inference in particular. (Causality is far more complicated and harder to discover than a lot of the stuff I work towards though, which is all in biology.)
  - canjobear 6y ago
    > a "Bayesian inference" spreadsheet like tool http://probcomp.csail.mit.edu/software/bayesdb/ http://probcomp.csail.mit.edu/software/bayesdb/
  - sjg007 6y ago
    Thousands and millions of variables approaches deep learning w/ softmax. It is difficult to see how it would work any other way. I mean would you want to have a causal model of particles?
- lalaland1125 6y ago
  Why would Pearl's approach be better at scaling? If anything, the Rubin causal model scales wonderfully with more variables.
wavegeek 6y ago
This is a strange debate. They seem to be in violent agreement.
- anigbrowl 6y ago
  https://en.wikipedia.org/wiki/Narcissism_of_small_differences https://en.wikipedia.org/wiki/Narcissism_of_small_difference...
stewbrew 6y ago
Balance is the wrong word. you might want to check first who was AG's doctor father. Surprise, it was Rubin.

epistasis 6y ago

Thanks for sharing this! I knew that Gelman and Pearl have been going back and forth for years, and it's great to see this. I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction. That said, I think their disagreements don't have much effect on practice, and have as much to do with their different academic lineages as anything else. I expect that any dispute will be resolvable as the science progresses

elcritch 6y ago
> I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction. Are you able to elaborate?! It'd be really interesting to hear what/if tooling exists for these graphs. I just found the book recently and am listening to the Audible version. It also reaffirms my desire for a "Bayesian inference" spreadsheet like tool. Something that'd help organize a few dozen thoughts ideas for researchers/engineers.
- currymj 6y ago
  R package "daggity" is excellent. http://www.dagitty.net/ http://www.dagitty.net/
- epistasis 6y ago
  I've used libDAI in the past, but I'm not sure if it's maintained anymore. I've come across several other toolkits that looked interesting since then, but never had the right project. There's a group at (CMU?)/Pitt that I think now has a Python package for causal inference in particular. (Causality is far more complicated and harder to discover than a lot of the stuff I work towards though, which is all in biology.)
- canjobear 6y ago
  > a "Bayesian inference" spreadsheet like tool http://probcomp.csail.mit.edu/software/bayesdb/ http://probcomp.csail.mit.edu/software/bayesdb/
- sjg007 6y ago
  Thousands and millions of variables approaches deep learning w/ softmax. It is difficult to see how it would work any other way. I mean would you want to have a causal model of particles?
lalaland1125 6y ago
Why would Pearl's approach be better at scaling? If anything, the Rubin causal model scales wonderfully with more variables.

wavegeek 6y ago

This is a strange debate. They seem to be in violent agreement.

anigbrowl 6y ago
https://en.wikipedia.org/wiki/Narcissism_of_small_differences https://en.wikipedia.org/wiki/Narcissism_of_small_difference...

stewbrew 6y ago

Balance is the wrong word. you might want to check first who was AG's doctor father. Surprise, it was Rubin.