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I tried reading Pearl's "Causality" but it was too much for me. Anybody get through it? What did you think?

MrQuincle 10y ago
There will be a popular science book from him coming out, I read a draft a while ago. It will present it all in a digestible way. The do-calculus to me looks a bit cumbersome. I'm not generally attracted to math that doesn't look like math, except if it is nice looking. :-) On a more general level, when I'm building robots, I consider the interface between their minds and the world a Markov blanket. This means that state in the world can only be represented in the robot by learning through its interface by a combination of forward and inverse models. The existence of a physical barrier makes it possible to define information going through it. You might suggest that the world is created by the robot's mind, but that is probably difficult to back with any information criterion. If the robot learns its body, it knows that it is itself who is performing a movement or if it is being pushed. Knowing that it was itself the actor is enough to know the direction of the causal arrow.
- tr352 10y ago
  Can you tell me the name of that book that is coming out?
  - MrQuincle 10y ago
    https://books.google.nl/books/about/The_Book_of_Why.html?id=9H0dDQAAQBAJ&redir_esc=y https://books.google.nl/books/about/The_Book_of_Why.html?id=... I'm not sure if that's gonna be the final title though.
MrQuincle 10y ago
Interesting research that addresses some weaker points: https://yanirseroussi.com/2016/05/15/diving-deeper-into-causality-pearl-kleinberg-hill-and-untested-assumptions/ https://yanirseroussi.com/2016/05/15/diving-deeper-into-caus...
kgwgk 10y ago
"Causal Inference in Statistics: A Primer" is somewhat lighter (at least if you have an statistics background). https://www.amazon.com/Causal-Inference-Statistics-Judea-Pearl/dp/1119186846 https://www.amazon.com/Causal-Inference-Statistics-Judea-Pea...
petters 10y ago
The conclusions in the book are amazing. It is possible to infer casual relationships just from observing. But I couldn't help thinking that the presentation could be improved a little. I am not clever enough to figure out how; it's just a feeling I have. I recommend starting with the less wrong articles linked here. Eliezer Yudkowdky is great at explaining things.
- overeater 10y ago
  I read it a while ago and don't know that much about causality, but my conclusion was the opposite of the author's: that it's not possible to infer causal relationships from observations only. All the papers I have read that claim the magic phrase "causality just from observations" either redefine causality, or make some assumptions about what could be the cause. The way it was done in the book required some tricks. And the way the tricks worked was to make assumptions about how the causality flowed, and that the model captured all possible causes of an effect. For example, with the "does smoking causes lung cancer" example (back propagation), you had to say that smoking causes tar in the lungs, which is the only possible source of lunch cancer. (this might not be exactly right, my memory is a bit hazy now). I'd be happy to hear from someone who is more of an expert than I am.
  - petters 10y ago
    I think it's fairer to say that it is possible. I am on a train so I can't do more than refer to http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/ http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model... unfortunately.
    - overeater 10y ago
      After reading that, I think that post actually supports my claim and makes me think the same thing after reading Judea Pearl's book. There is a redefinition of causality going on, and a bunch of implicit assumptions, namely that the structure of causality that is created is correct. Basically, if you can generate a bunch of nodes and arrows that are correct and the only possible causal explanations, then sure you can compute causality given these assumptions. The comments in that article point out these same problems.

There will be a popular science book from him coming out, I read a draft a while ago. It will present it all in a digestible way. The do-calculus to me looks a bit cumbersome. I'm not generally attracted to math that doesn't look like math, except if it is nice looking. :-) On a more general level, when I'm building robots, I consider the interface between their minds and the world a Markov blanket. This means that state in the world can only be represented in the robot by learning through its interface by a combination of forward and inverse models. The existence of a physical barrier makes it possible to define information going through it. You might suggest that the world is created by the robot's mind, but that is probably difficult to back with any information criterion. If the robot learns its body, it knows that it is itself who is performing a movement or if it is being pushed. Knowing that it was itself the actor is enough to know the direction of the causal arrow.

tr352 10y ago
Can you tell me the name of that book that is coming out?
- MrQuincle 10y ago
  https://books.google.nl/books/about/The_Book_of_Why.html?id=9H0dDQAAQBAJ&redir_esc=y https://books.google.nl/books/about/The_Book_of_Why.html?id=... I'm not sure if that's gonna be the final title though.

MrQuincle 10y ago

Interesting research that addresses some weaker points: https://yanirseroussi.com/2016/05/15/diving-deeper-into-causality-pearl-kleinberg-hill-and-untested-assumptions/ https://yanirseroussi.com/2016/05/15/diving-deeper-into-caus...

kgwgk 10y ago

"Causal Inference in Statistics: A Primer" is somewhat lighter (at least if you have an statistics background). https://www.amazon.com/Causal-Inference-Statistics-Judea-Pearl/dp/1119186846 https://www.amazon.com/Causal-Inference-Statistics-Judea-Pea...

petters 10y ago

The conclusions in the book are amazing. It is possible to infer casual relationships just from observing. But I couldn't help thinking that the presentation could be improved a little. I am not clever enough to figure out how; it's just a feeling I have. I recommend starting with the less wrong articles linked here. Eliezer Yudkowdky is great at explaining things.

overeater 10y ago
I read it a while ago and don't know that much about causality, but my conclusion was the opposite of the author's: that it's not possible to infer causal relationships from observations only. All the papers I have read that claim the magic phrase "causality just from observations" either redefine causality, or make some assumptions about what could be the cause. The way it was done in the book required some tricks. And the way the tricks worked was to make assumptions about how the causality flowed, and that the model captured all possible causes of an effect. For example, with the "does smoking causes lung cancer" example (back propagation), you had to say that smoking causes tar in the lungs, which is the only possible source of lunch cancer. (this might not be exactly right, my memory is a bit hazy now). I'd be happy to hear from someone who is more of an expert than I am.
- petters 10y ago
  I think it's fairer to say that it is possible. I am on a train so I can't do more than refer to http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/ http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model... unfortunately.
  - overeater 10y ago
    After reading that, I think that post actually supports my claim and makes me think the same thing after reading Judea Pearl's book. There is a redefinition of causality going on, and a bunch of implicit assumptions, namely that the structure of causality that is created is correct. Basically, if you can generate a bunch of nodes and arrows that are correct and the only possible causal explanations, then sure you can compute causality given these assumptions. The comments in that article point out these same problems.