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by bluefox
1936 days ago
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I've read this book twice. The first time, I enjoyed it, and as I read it I felt that I understood the gist, at least intuitively. Similar to what you describe, my view was always that statistics is a bunch of tricks, and probability is much deeper. After reading this book, I read another book, a technical book about probabilistic graphical models. As I read the book, I implemented most of the algorithms. I also had to read a bunch of papers from the 80s and 90s to do that. I then decided to read this book a second time, and now I really came to appreciate Pearl's points, and can see why statistics (and probability...) are insufficient, and the need for his do-calculus. I've also been reading much of his 1988 classic, though not done with it. While I'm not there yet (still implementing more papers, and not read his Causality book yet) I can see how his proposed calculus and the work of his students in that area can help do the things he describes in the last chapter. So, the book can be interesting to lay people, and it may entice them to learn more. I think this is the book's purpose, and therefore that it is a success, at least with me. |
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All you need to do to verify that his claims about statisticians is BS is look at the potential outcomes framework, which was first developed in 1923: https://en.wikipedia.org/wiki/Rubin_causal_model
He's well within his rights to argue that the PO framework has limitations and that his framework is superior. It's unethical for him to claim that statisticians are anti-causality or that they never studied it.