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The Book of Why: The New Science of Cause and Effect

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Richard S. This book is about the significance of Bayes' Theory and Bayesian networks in trying to better understand causality. The initial issue is that saying …m´Ç°ù±ðThis book is about the significance of Bayes' Theory and Bayesian networks in trying to better understand causality. The initial issue is that saying that we know "nothing" about some matter or some process until we do a bunch of tests or trials is patently false, and plainly an extreme and unwarranted claim or contention. Saying we know nothing is just as bad, and maybe worse, than saying we know all about something without doing any tests.

Bayes' initial contention was that if he blindly rolled a billiard ball on a table, without any idea of where it might land or end up, it would probably (meaning most of the time) end up somewhere near the middle of the table. Not at an edge, meaning an extreme position. And not at the precise middle of the table. And the ball is on the table.

This is a basis for proposing that past experience is of some value in interpreting data and leads to a better understanding of causality itself. It is not an easy book, and the book proposes how we can and should deal with causality. It also proposes a mathematical structure for doing so. It is a book for anyone who still thinks or who has ever questioned that "correlation is not causation" is entirely valid. Of course, the reality is that correlation has some relation to causality. We know that, so how does it really work? And then: How can we structure our thinking on this matter?

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