Elements of Causal Inference: Foundations and Learning Algorithms - 版本历史

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内容简介-英文

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

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 =内容简介-中文=
 《因果推理：基础与学习算法》从概率统计的角度入手，分析了因果推理的假设，揭示这些假设所暗示的因果推理和学目的。本书分别论述了变量和多变量情况下的因果模型、学习因果模型及其与机器学关系，讨论了因果推理隐藏变量有关的问题、时间系列的因果分析。 《因果推理：基础与学习算法》可作为高等院校人工智能和计算机科学等相关专业高年级本科生和硕士研究生的教材，也可供研究机器学习、因果推理的技术人员参考。
+[[文件:《因果推理：基础与学习算法》.jpg|缩略图|因果推理：基础与学习算法]]
 =基本信息=

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-[[Elements_of_Causal_Inference_Foundations_and_Learning_Algorithms.jpeg|缩略图|Elements of Causal Inference  Foundations and Learning Algorithms]]
+[[文件:《Elements of Causal Inference- Foundations and Learning Algorithms》.jpg|缩略图|MElements of Causal Inference  Foundations and Learning Algorithms]]
 =内容简介-英文=
        The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.

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-[[文件:Elements of Causal Inference Foundations and Learning Algorithms|缩略图|Elements of Causal Inference  Foundations and Learning Algorithms]]
+[[Elements_of_Causal_Inference_Foundations_and_Learning_Algorithms.jpeg|缩略图|Elements of Causal Inference  Foundations and Learning Algorithms]]
 =内容简介-英文=
        The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.

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-[[文件:《Elements of Causal Inference: Foundations and Learning Algorithms》.jpg|缩略图|《Elements of Causal Inference: Foundations and Learning Algorithms》]]
+[[文件:Elements of Causal Inference Foundations and Learning Algorithms|缩略图|Elements of Causal Inference  Foundations and Learning Algorithms]]
 =内容简介-英文=
        The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.

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 *[[因果科学社区]]
 =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'''
-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]]]
 ===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]====

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 Jonas Peters是哥本哈根大学数学科学系的统计学教授。此前，他曾担任图宾根马克斯-普朗克智能系统研究所的组长和苏黎世联邦理工学院统计研讨会的玛丽居里研究员。他在海德堡大学和剑桥大学学习数学，并在 MPI 和 ETH 联合获得博士学位。他感兴趣的是从不同类型的数据中推断因果关系，以及建立对分布变化具有鲁棒性的统计方法。在他的研究中，乔纳斯试图将理论、方法和应用结合起来。他的工作涉及计算统计、因果推理、图形模型、独立性测试或高维统计等领域。
 '''Dominik Janzing'''
 Dominik Janzing是位于德国图宾根的马克思·普朗克智能系统研究所的高级研究科学家，现在致力于从统计数据进行因果推理和新的因果推理规则的基础。
 '''Bernhard Schölkopf'''