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*[[因果科学社区]]

*[[因果科学社区]]

=编者推荐=

=编者推荐=

===豆瓣书评===

===豆瓣书评[https://book.douban.com/subject/27088754/]===

====[https://book.douban.com/review/14067788/ 一次减法带来的科学革命]====

“又是一本当年自己没看懂觉得很无聊 现在发现是神作的书……” ——'''一般翼赞员'''

“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

“I read this book because I was so impressed with the importance of causal inferencing from data, as Pearl has developed it, in his more popular book, "The Book of Why". But that book didn't present enough detail for me to actually apply causal analysis to my own data. I hoped that a self-styled "Primer" would fill that gap.

Unfortunately, this book did not help......” ——James Foster

“主要就介绍作者们这几年发的论文工作，除此以外的部分就是宽泛的文献综述了。没太多细节。” ——'''周末'''

“......So yes, I guess I'm drinking the Pearl Kool-aid. This book deserves such praise in my opinion, though, for having the informative lack of fluff of a textbook without the soporific dryness of one” ——Anthony DiGiovanni[[File:因果推断.png|400px|right|thumb|[https://campus.swarma.org/course/1798 因果推理与机器学习读书会]]]

 

===Goodreads书评[https://www.goodreads.com/book/show/34889379-elements-of-causal-inference?from_search=true&from_srp=true&qid=iaibbz7Qht&rank=1]===

 

“This book provides a nice introduction into today's causal inference research. For a person like me who is vaguely interested in the topic, but 1) find classical writings like Pearl's to be difficult to understand because they are not written in the language of modern statistics & machine learning, and 2) want to get an overview of today's rapid & diverse research on the topic, this book is a perfect fit. Authors explain key ideas of causal inference in modern terminologies of machine learning, and I found it much more readable than others. They also cover a wide spectrum of ongoing approaches and issues in the field, and make insightful connections between them. Since the book covers so many topics, however, most topics are only sketchily touched, and technical proofs are mostly left out. Moreover, authors concentrate mostly on theoretical issues (ex: identifiability) and applications to real-world problems are only occasionally discussed. This book only serves as a starting point, and you need to follow references to really understand any topic; I expected deeper and gentler dive, at least for key concepts. I also found latter half of the book to be not as carefully written as in the beginning; so many parentheses and hyphens, which are quite distracting.”  ——'''Hyokun Yun'''

 

 

“After reading "The Book of Why", I was looking for a more technical introduction to causality. Since by background in machine learning using kernel methods, this book co-authored by Bernhard Schölkopf seemed a good start.Though I skimmed through the latter chapters, the beginning gives a good introduction to the different types of causality and which assumptions that have to be made. I especially liked the chapters drawing links between causality and topics like transfer learning and domain adaptation!” ——'''Michiel'''[[File:因果推断.png|400px|right|thumb|[https://campus.swarma.org/course/1798 因果推理与机器学习读书会]]]

===集智俱乐部读书会推荐===

===集智俱乐部读书会推荐===

====[https://campus.swarma.org/course/1798 因果推理与机器学习读书会]====

====[https://campus.swarma.org/course/1798 因果推理与机器学习读书会]====