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I've been reading the 'book of why' by Judea Pearl and come to understand that Bayesian Networks can be used to establish causality given a directed acyclic graph (DAG) and that the methods are non-parametric. Throughout the book, the author drags Pearson and Fisher through the mud; it can be hard to tell what is an emotional reaction to resistance from the stats community vs genuine criticisms/improvements to traditional stats approaches to causal inference.

My question is: How are traditional approaches from stats different?

jbuddy_13
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  • I have a similar sentiment to the one expressed in your second sentence. But your question seems a bit too broad. – Richard Hardy Dec 03 '21 at 16:47
  • @RichardHardy I see one upvote and two votes to close, so I edited and reduced scope of question – jbuddy_13 Dec 03 '21 at 17:00
  • I've retracted my close vote, but I still think the question is a bit broad. Pearl himself outlines many of the differences between his approach and trad. stats. I wouldn't say Pearl "drags Pearson and Fisher through the mud"; he does critique their approach, but I think he's fair and says when he thinks they did the right thing. – Adrian Keister Dec 03 '21 at 17:13

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