After having read Book Of Why and Causal Inference In Statistics – A Primer, I was reading Causality – all by Judea Pearl.
Yet I found that there were quite some points which I was not understanding. Particularly, Markov and Bayesian networks, axiomatization of probabilistic system using information relevance axioms, etc. However, having followed the references given to Probabilistic Reasoning In Intelligent Systems by Pearl, they were much more clear.
Hence, is Probabilistic Reasoning In Intelligent Systems, or parts of it, a pre-requisite of Causality? If yes, which parts might be good to go through?
Best Answer
I have only read Causality (2nd ed) from Judea Pearl. I would say that, at a minimum. a graduate level course in probability and statistics is the only "pre-req". I would say each of Judea's texts are intended for different audiences, rather than meant to comprise a didactic arc. Judea's approach in Causality is highly technical, and follows much the same line of reasoning as Rubin's Potential Outcomes Framework https://www.tandfonline.com/doi/abs/10.1198/016214504000001880. Judea and Rubin specifically develop an idea of causality as an "algebraic" concept, i.e. I can "do" anything conceptually. For a softer take that represents a different perspective, consider Hernan and Robin's book "Causal Inference" which I consider to be excellent https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/