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Valuable for your permanent for ongoing reference and inspirational revisiting, with an absolutely ideal annotated bibliography. Artisan crafting to certainly withstand the test of time.

Invest 2-3 days in simplifying and repairing how you think causally! I’m sure glad I did. Fun and readable, and so practically valuable. The brilliant core tenet: data are dumb, people’s models can be smart. Knowledge is in the model, not just waiting to emerge from the data. Wow.. that contradicts completely what modern world correlation economists, policy analysts and social scientists have taught for several decades!

On pg 64-65, Pearl elegantly distinguishes two models of how talent and luck generate success. If luck applies independently to each degeneration, the model is mathematically stable, but it it accrues (with talent) over generations you get a wide persistent distribution of outcomes. For me, this profoundly simplified economic inequality, and shows how useful is a rigorous framework for thinking. Without accruing “luck� you get reversion to the mean, and with it you get dynastic wealth. Yet with either one, talent is passed down generations, aiding success under equal opportunity.

Appropriately, in the engaging historical chapter 2, Pearl often asks Why historical persons thought as they did, whenever he is able to answer himself. These Why’s show how important causation is to understanding anything of importance. It helps the storytelling that the eminent statisticians Karl Pearson & Fisher are (through arrogance and dominance) the scientific bad guys. And it is consistent to see Fisher return in his evil cantankerous role during the tobacco trials and as a professor of Eugenics.

Pages 104-7 provide the most simple and lucid explanation of estimating the likelihood of having a disease from a positive test result for it. So useful.

Fun seeing how fuzzy math and Bayesian networks, cutting edge research when I was in grad school, have evolved into the mainstream. I can almost imagine my alternative life, had I studied this and developed ways to use it. 30 years ago honestly, I couldn’t have imagined it would be as widespread as it is today. Partly because back then we had no big data.

The paradox chapter is fascinating, and especially meaningful because it anchors the earlier theoretical ideas into memory.

The closing chapters are difficult, in terms of figuring out how to apply this sparkling and sharp tools to your own life and work. Well, I suppose that should be a challenge. I wish more work and opinions from outside of Pearl’s immediate academic lineage were included, since I don’t feel we are given a clear view of whether there are any, and how his work fits into the overall future of science.

Final chapter on Ai is only a start, and leaves little on which to build. Author’s assumption that a moral Ai could resolve all control problems feel shallow to me, however attractive! His assertion, that free will is superior in performance to borg like behavior from simulations like generative adversarial networks, feels unproven.