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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 relat
by 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 agoI 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 agoAfter 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.