> This book dwells on the history of statistics a lot, and statisticians, as the authors would have you believe, are zealots who have conspired to keep causal thinking out of their field right from the start. That is, until Pearl instigated the "Causal Revolution", as he dubs it, the latest and greatest gift to modern science. I have no dog in this fight, but Pearl (whom I assume is the source of most of these opinions put to paper by Mackenzie) often comes across as wildly biased and grandiose. For what it's worth, I doubt that statisticians as a whole are anywhere as malicious or ignorant as they're portrayed in this book.
This is correct AFAICT (I'm not a statistician even though I read a lot of the statistics literature). The strange thing is that I've never seen any obvious benefits to his comments of this nature. In the most generous possible reading, they are a distraction, with a less generous reading being that you can't trust his interpretation of anything.
The “benefit” is that Pearl’s causality is reinventing the wheel, and his approach is a smokescreen for this. Causal inference has been a thing in statistics since the 1930s. There is absolutely nothing Pearl has developed here that makes a practical difference when it comes to actually establishing causality over methods we’ve had for a century. So to prop up his contributions to the causal “revolution”, he attempts to paint a picture where statisticians are a hopelessly backward, regressive group, and hopes the uninitiated won’t know any better. It seems like he has largely succeeded.
Well, I read the book. I like his clear style, he tries to get across what is different about his approach. To be honest, I like probability theory, but never thought much of statistics, so I guess I can sympathise from where he is coming from.
I enjoyed this book much more than any text about statistics I ever read.
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.
I think the problem with your comment (and this is why it's necessary to post these qualifications any time Pearl comes up) is that you've bought into Pearl's claims about statistics. Statisticians have been studying causality for a long time.
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.
You seem to read too much into my comment, and make it into something partisan. I'm not interested in that debate. My thoughts about statistics and probability were there before I read Pearl's book. Pearl mentions potential outcomes in his book.
The battle between frequentist statisticians and those advocating Bayesian approaches is quite old—-back at least to Wright. Pearl is not inventing a dichotomy but explaining why models are necessary to evaluate causality. Is this genuine progress? Absolutely! Causal Bayesian modeling is transformative.
Every experimentalist and clinician will come away with good from Book of Why even if the tone rubs some the wrong way occasionally. I made this required reading in my human genetics course for grad students. Perfect level. Yes, I got some welcome pusback from bright students, but I know this book will have an indelible positive impact on their depth of thinking about data generation, model assumptions, confounders, interventions, and counterfactuals.
This is correct AFAICT (I'm not a statistician even though I read a lot of the statistics literature). The strange thing is that I've never seen any obvious benefits to his comments of this nature. In the most generous possible reading, they are a distraction, with a less generous reading being that you can't trust his interpretation of anything.