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===豆瓣书评===
====[https://book.douban.com/review/14067788/ 一次减法带来的科学革命]====
“一直都不太看好所谓的归因分析, 也不太理解为什么归因分析会作为图灵奖评审标准. 归因分析其实就是概率减法而已, 这是个再简单不过的概念.归因分析近年来受到了互联网公司的关注. Uber 就是其中之一.理论科学有的时候只要稍稍有一些创新概念, 就会带来应用领域的革命, 所以不要放过自己每一次的灵光一现.” ——Hao
===Goodreads书评===
====[https://book.douban.com/review/1314955/ 一本被主流遗忘的经典？]====
“In this short book, Pearl gives a broad yet detailed introduction to his flavor of causal inference. That means back-doors and front-doors and graphs as the foundation with potential outcomes and counterfactuals as the fruits to be harvested.
For a scientist, Pearl is an outstandingly good writer. Even his journal articles are readable! His popular book (The Book of Why: The New Science of Cause and Effect) was quite enjoyable and this textbook is concise and clear. I read this book at the same time as I was reading Causal Inference: What If and found that, for the concepts that are covered in both, Pearl's presentation was more clear.
The book has only four chapters. Chapter 1 gives an overview of causal inference and a brief overview of important background from probability theory. Chapter 2 lays out the graphical approach to structural causal models and introduces d-separation. Chapter 3 shows how to connect the graphical models to causal questions, with the central focus being back-door and front-door corrections, including a section on inverse probability weighting and linear models/regression. Chapter 4 expands the computational methods to a wider array of counterfactual questions, ending with extensive examples and explicit formulas for direct and indirect effects under mediation.
The greatest weakness of "The Book of Why" is that the examples are not worked out clearly. After reading it, you are convinced that there is something to this causal inference thing, but you have no intuition as to how to solve even the simplest problem involving confounders or colliders. In that sense, the current book is a worthy sequel. If you follow the equations and exercises (not particularly difficult), you will know how to solve simple causal inference problems.
That raises the question of who this book is intended for. I found the book to be challenging, but not bewildering or unnecessarily complex. I should note that I have a Ph.D. in physics and have worked for several years in something like "data science." In theory the book is intended for undergraduates who are studying "elementary statistics." I guess they would get something out of it, but my guess is that a beginning student would have difficulty appreciating what the relevance of these methods is without familiarity with how things are treated in the absence of them. After all, the math itself is rather simple, comfortably at the level of a precocious undergrad. What makes the subject challenging is understanding how to translate back and forth between assumptions about reality, graphs/equations and numerical estimates.
Unfortunately, there are a large number of typos and other minor errors in the book. He has a corrected pdf on his website if you're interested. Also, the course website is not whatever is written in the text, you need to Google it.
One last point is that there is an ongoing struggle within the causal inference community between Pearl's approach and the Neyman's potential outcomes approach. You'll see it obliquely referenced here, in Hernan's book (from the other side), in sparring journal articles and--of course--on academic Twitter. I kind of wish the argument would be less partisan and more philosophical but I guess even scientists are human beings. To get a sense of what the argument is about, I'd read https://ftp.cs.ucla.edu/pub/stat_ser/...。”——Michael
===集智相关文章===
在人工智能盛行的今天，当绝大多数人仅对“奇巧淫技”感兴趣的时候，集智俱乐部重新编辑推出冯·诺依曼自动机理论这系列文章，这是著名人工智能先驱Arthur Burks整理的冯·诺依曼的手稿《自复制自动机理论》的第一部分，该部分详细论述了思考自复制自动机的动机与意义。该文后经由集智俱乐部资深粉丝“东方和尚”翻译成中文，并由张江注释后奉献给广大读者。希望通过溯本清源，我们能够重新追溯冯·诺依曼的思考轨迹，从全新的视角审视人造机器的生命本质。
[[File:大数之道.jpeg|400px|right|thumb|[https://swarma.org/?p=3723 《自复制自动机理论》集智解读]]]
====[https://swarma.org/?p=3723 冯·诺依曼的遗产：寻找人工生命的理论根源]====
:《自复制自动机理论》前言1：冯·诺依曼在计算机方面的工作
====[https://swarma.org/?p=3678 冯·诺依曼：探寻计算的“原力”]====
:《自复制自动机理论》前言2：冯·诺依曼的自动机理论
====[https://swarma.org/?p=3652 神经网络与图灵机的复杂度博弈]====
:《自复制自动机理论》第一堂课：一般意义的计算机
====[https://swarma.org/?p=3633 人工智能如何掷骰子——三种概率理论]====
:《自复制自动机理论》第二堂课：控制与信息理论
====[https://swarma.org/?p=3145 信息的统计理论：复杂度阈值与概率论中“漏洞”]====
:《自复制自动机理论》第三堂课：信息的统计理论
====[https://swarma.org/?p=3216 大数之道——人脑与电脑的对比]====
:《自复制自动机理论》第四堂课：大数之道
====[https://swarma.org/?p=3128 自指机器的奥秘]====
:《自复制自动机理论》第五堂课：复杂自动机的一些考量——关于层次与进化的问题
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