I'm looking to get a better understanding on causality, in particular with relation to AI. Judea Pearl has written at least 3 books on the subject.
I have already read The book of why - this one wasn't technical enough. Should I read Causality: Models, Reasoning and Inference or Causal Inference in Statistics - A Primer. If somebody is familiar with both of these books, how do they compare?
I looked at these several months ago and unfortunately recommend neither. Pearl's Causality is very dense, and not really a good introduction. The Primer is really egregiously riddled with errors; there seems to have been some problem with the publisher. And on top of that, I just found it not very well written.
I don't have a specific recommendation, but I believe that at this point there are a bunch of statistics textbooks that competently discuss the essential content of causal modelling; maybe check the reviews for some of those on amazon.
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1Causal Inference in Statistics (pdf) is much shorter, and a pretty easy read.
I have not read causality but I think you should probably read the primer first and then decide if you need to read that too.
(Commenting rather than answering because I haven't read the books I talk about.)
People have recommended Causation, Prediction and Search to me, but I haven't read it (yet), mostly due to the fact that it doesn't have exercises. Probabilistic Graphical Models talks about causality in chapter 21 (pg. 1009-1059).
Causal Inference in Statistics (pdf) is much shorter, and a pretty easy read.
I have not read causality but I think you should probably read the primer first and then decide if you need to read that too.