I also enjoyed his discussion of smoking, the Bradford Hill criteria, and how we needed to move beyond mere correlation and association to make informed decisions. Pearl’s ability to place these discussions in a historical context, highlighting the contributions of figures like Fisher and Sewall Wright, was impressive.
I particularly appreciated how Pearl explained the three key graphical elements in Bayesian networks, which he referred to as chains (A leads to B, which leads to C), colliders (A and C both point to B), and forks (B leads to both A and C). He explained how these motifs differ in terms of the independence of the three variables and the extent to which they remain independent when conditioned on B. He provided intuitive examples to help readers easily grasp these concepts and emphasized the fundamental problem of confounding in statistical design, as well as how randomized controlled trials address this issue.
However, at times, I found it challenging to distinguish between background information on established concepts and Pearl’s more polemical arguments advocating for what he calls the “causal revolution,” which may be less widely accepted in the statistical and AI communities. This occasionally led to some confusion, but overall, I enjoyed the book immensely and would highly recommend it to any reader.
https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X
The Book of Why: The New Science of Cause and Effect
by Pearl, Judea