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If I've earned a penny every time I heard the sentence "correlation is not causation", I'd be a richer man by now, and that'd probably be a causal relationship.

If correlation is not causation, then what is causation? I, like many others, asked this question since I took my first undergraduate courses in probability and statistics back in 1990s. I, like many other curious souls, couldn't see a strong, rigorous mathematical answer, and later in life, at least for me, the topic of counterfactuals seemed to be relegated to a niche philosophical subfield.

Still later, when I started to deal with technological topics such as big data, as well as data science, I heard strong voices saying that what mattered was predictive power, and what did "really understanding something" even mean? Well, I thought, understanding something should mean constructing meaningful logical sentences that included at leas one well-grounded "because". Of course, being able to really understand things didn't matter much if the problem at hand was about guessing what a particular user would buy next, or watch next. It also didn't matter much if a black box could very accurately transcribe speech. But for many fields of data analysis, such as medicine, public policy, education, etc. I still find it very important to really understand things, that is, being able to talk about Ys causing Xs.

Finally, I have a beautiful exposition by one of the luminaries and revolutionaries of the causal revolution. The book doesn't help much in "discovering" causal relationships, but its focus on what it means to have a mathematical and solid graphical, algorithmic language to talk and communicate causation points to an achievement that is nothing less than a stellar. It also helped me to understand what Pearl has achieved after Bayesian networks, showing in many examples what it means to have "directionality", which was lacking in previous representations.

If the discussions and criticisms among statisticians such as and are any indication, this book will continue to generate much more heated discussion in the near future, that's for sure.

I can recommend this book to anyone involved with any scientific field that uses statistics, as well as people dealing with big data, machine learning, deep learning, and data science: the insights to be gained are invaluable. In a sense, this book marks the end of an intellectual tour de force by one of the greatest minds of reasoning. In another sense, this book also marks the perfectly solid starting point for the next phase of our long journey in understanding nature, including ourselves.