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第66行： − Chapter 2 starts with a brief summary of the contributions of Francis Galton and Karl Pearson to the development of statistics in the late 19th Century and early 20th Centuries. The authors blame Galton for keeping the study of statistics on the first rung of the ladder of causation and discouraging any discussion of causality in statistics. Causal analysis using path diagrams is then introduced through the explanations of the work of Sewall Wright.+ − 第二章首先简要总结了弗朗西斯·高尔顿和卡尔·皮尔森在19世纪末20世纪初对统计学发展的贡献。作者们指责高尔顿把统计学的研究放在了因果关系阶梯的第一级，并阻止了统计学中任何关于因果关系的讨论。使用路径图的因果分析，然后介绍了通过解释Sewall Wright的工作。+ − Chapter 3: From Evidence to Causes: Reverend [Thomas Bayes|Bayes] meets Mr Holmes[edit]+ − Chapter 3 provides an introduction to Bayes Theorem. Then Bayesian Networks are introduced. Finally, the links between Baysian networks and causal diagrams are discussed.+ − Chapter 4: Confounding and Deconfounding, or, Slaying the Lurking Variable[edit]+ − This chapter introduces the idea of confounding and describes how causal diagrams can be used to identify confounding variables and determine their effect. Pearl explains that randomized controlled trials (RCTs) can be used to nullify the effect of confounders, but shows that, provided one has a causal model of confounding, an RCT does not necessarily have to be performed to get results.+ − Chapter 5: The Smoke-filled Debate: Clearing the Air[edit]+ − This chapter takes a historical approach to the question 'does smoking cause lung cancer?', focusing on the arguments made by Abraham Lilienfeld, Jacob Yerushalmy, Ronald Fisher and Jerome Cornfield. The authors explain that, though cigarette smoking was clearly correlated with lung cancer, some, such as Fisher and Yerushalmy, believed that the two variables were confounded and argued against the hypothesis that cigarettes caused the cancer. The authors then explain how causal reasoning (as developed in the rest of the book) can be used to argue that cigarettes do indeed cause cancer.+ − Chapter 6: Paradoxes Galore![edit]+ − This chapter examines several paradoxes, including the Monty Hall Problem, Simpson's paradox, Berkson's paradox and Lord's paradox. The authors show how these paradoxes can be resolved using causal reasoning.+ − Chapter 7: Beyond Adjustment: The Conquest of Mount Intervention[edit]+ − This chapter looks at the 'second rung' of the ladder of causation introduced in chapter 1. The authors describe how to use causal diagrams to ascertain the causal effect of performing interventions (eg. smoking) on outcomes (such as lung cancer). The 'front-door criterion' and the 'do-calculus' are introduced as tools for doing this. The chapter finishes with two examples, used to introduce the use of instrumental variables to estimate causal relationships. The first is John Snow's discovery that cholera is caused by unsanitary water supplies. The second is the relationship between cholesterol levels and likelihood of a heart attack.+ − Chapter 8: Counterfactuals: Mining worlds that could have been[edit]+ − This chapter examines the third rung of the ladder of causation: counterfactuals. The chapter introduces 'structural causal models', which allow reasoning about counterfactuals in a way that traditional (non-causal) statistics does not. Then, the applications of counterfactual reasoning are explored in the areas of climate science and the law.+ − Chapter 9: Mediation: The Search for Mechanism[edit]+ − This chapter discusses mediation: the mechanism by which a cause leads to an effect. The authors discuss the work of Barbara Stoddard Burks on the causes of intelligence of children, the 'algebra for all' policy by Chicago public schools, and the use of tourniquets to treat combat wounds.+ − Chapter 10: Big Data, Artificial Intelligence and the Big Questions[edit]+ − − The final chapter discusses the use of causal reasoning in big data and artificial intelligence (AI) and the philosophical problem that AI would have to reflect on its own actions, which requires counterfactual (and therefore causal) reasoning. − Scientific Background, excerpts, errata, and a list of 37 reviews of The Book of Why is provided on Judea Pearl's web page + − The Book of Why was reviewed by Jonathan Knee in The New York Times. The review was positive, with Knee calling the book "illuminating". However, he describes some parts of the book as "challenging", stating that the book is "not always fully accessible to readers who do not share the author's fondness for equations".+ − Tim Maudlin gave the book a mixed review in The Boston Review, calling it a "splendid overview of the state of the art in causal analysis". However, Maudlin criticizes the inclusion of "counterfactuals" as separate rung on the "ladder of causation", stating counterfactuals are so closely entwined with causal claims that it is not possible to think causally but not counterfactually". Maudlin also criticizes the section on free will for its "imprecision and lack of familiarity with the philosophical literature". Finally he points to the work of several scientists (including Clark Glymour) who developed similar ideas to Pearl, and claims that Pearl "could have saved himself literally years of effort had he been apprised of this work".+ − In a rebuttal, Pearl notes that, not only was he apprised of these scientists' work, but he actively collaborated in its creation. Additionally, the key developments described in The Book of Why, among them (1) identification analysis, (2) the algorithmization of counterfactuals, (3) mediation analysis, and (4) external validity, far surpass the narrow philosophical literature of the pre-2000 era.+ − Zoe Hackett, writing in Chemistry World, gave The Book of Why a positive review, with the caveat that "[i]t requires concentration, and a studious effort to work through the mind-bending statistical problems posited in the text". The review concludes by stating that "[t]his book is a must for any serious student of philosophy of science, and should be required reading for any first-year undergraduate statistics class".+ − Lisa R. Goldberg wrote a detailed, technical review in Notices of the American Mathematical Society.+
无编辑摘要
[[文件:The Book of Why.jpg|缩略图|
[[文件:The Book of Why.jpg|缩略图|
{| class="wikitable"
{| class="wikitable"
!Authors
!作者
|Judea Pearl and Dana Mackenzie
|朱迪亚·珀尔和达纳·麦肯齐
|-
|-
!Language
!语言
|English
|英语
|-
|-
!Subjects
!科目
|Causality, Causal Inference, Statistics
|因果关系，因果推理，统计学
|-
|-
!Publisher
!出版商
|Basic Books (US)Penguin (UK)
|Basic Books(美国)、Penguin (英国)
|-
|-
!Publication date
!出版日期
|2018
|2018
|-
|-
|9780141982410
|9780141982410
|-
|-
!Preceded by
!支持
|''Causal Inference in Statistics: A Primer''
|''统计学中的因果推理:入门''
|}
|}
]]
]]
第二章：从海盗到豚鼠：因果推断的起源
第二章：从海盗到豚鼠：因果推断的起源
第二章首先简要总结了弗朗西斯·高尔顿和卡尔·皮尔森在19世纪末20世纪初对统计学发展的贡献。他们指责高尔顿把统计学的研究放在了因果关系阶梯的第一级，并阻碍了统计学中任何关于因果关系的讨论。在这之后休厄尔·赖特通过使用路径图将因果分析引入。
第三章：从证据到原因：当贝叶斯牧师遇见福尔摩斯先生
第三章介绍贝叶斯定理、贝叶斯网络，以及贝叶斯网络与因果图之间的联系。
第四章：混杂和去混杂：或者，消灭潜伏变量
本章介绍了混杂的概念，并描述了因果图如何被用来识别混杂变量并确定它们的效应。珀尔解释说，可以使用随机对照试验(RCT)来消除混杂因素的影响，也能使用其他因果模型获得同样的目的。
第五章：烟雾缭绕的争论：消除迷雾，澄清事实
本章从历史角度探讨“吸烟会导致肺癌吗?”这一问题，主要关注亚伯拉罕·利连菲尔德、雅各布·耶鲁沙米、罗纳德·费舍尔和杰罗姆·康菲尔德的观点。如费舍尔和耶鲁沙米解释说，虽然吸烟与肺癌明显相关，认为这两个变量混淆了，不同意香烟导致癌症的假设。随后，作者解释了因果推理(如本书其余部分所述)如何被用来论证香烟确实会导致癌症。
第六章：大量的悖论！
本章探讨了几个悖论，包括蒙蒂·霍尔问题、辛普森悖论、伯克森悖论和洛德悖论。作者展示了如何使用因果推理来解决这些悖论。
第七章：超越统计调整：征服干预之峰
本章着眼于在第一章中介绍的因果关系阶梯的“第二梯级”。作者描述了如何使用因果图来确定实施干预的因果效果。吸烟对结果(如肺癌)的影响。“前门准则”和“微积分”被作为工具引入。本章以两个例子结束，用来介绍使用工具变量来估计因果关系。第一个是约翰·斯诺发现霍乱是由不卫生的供水引起的。第二个是胆固醇水平和心脏病发作可能性之间的关系。
第八章：反事实：探索关于“假如”的世界
本章考察因果关系阶梯的第三级:反事实。本章介绍了“结构性因果模型”，它考虑以传统(非因果)统计方式不能对反事实进行推理的问题。然后，探讨反事实推理在气候科学和法律领域的应用。
第九章：中介：寻找隐藏的作用机制
这一章讨论的是调节:一因导致一果的机制。作者讨论了芭芭拉·斯托达德·伯克斯关于儿童智力成因的工作，芝加哥公立学校的“全民代数”政策和使用止血带治疗战争伤口的例子。
第十章：大数据，人工智能和重要问题
最后一章讨论了因果推理在大数据和人工智能(AI)中的应用，以及人工智能必须反思自身行为的哲学问题，而这需要反事实(因果)推理。
== 致谢 ==
== 致谢 ==
朱迪亚·珀尔的网页上提供了科学背景、摘录、勘误表和《为什么》一书的37篇评论
《为什么》一书由乔纳森在《纽约时报》进行评论，这篇评论是正面的，尼称这本书“具有启发性”。然而，他将这本书的某些部分描述为“具有挑战性的”，称这本书“对于那些不像作者那样喜欢数学方程的读者来说，并不总是能够完全理解”。
蒂姆·莫德林在《波士顿评论》上对这本书发表了褒奖不一的评论，称其为“对因果分析技术现状的精彩概述”。然而，莫德林批评将“反事实”作为“因果阶梯”上的一个单独的梯级，他指出，反事实与因果主张是如此紧密地交织在一起，以至于不可能只考虑因果而不考虑反事实。莫德林还批评了关于自由意志的部分，因为它“不精确，对哲学文学不够熟悉”。最后，他提到了几位科学家的工作(包括克拉克·格利莫尔)，他们提出了与珀尔类似的想法，并声称珀尔“如果被告知这项工作，他本可以节省多年的努力”。
在反驳中，珀尔指出，他不仅被告知了这些科学家的工作，而且还积极参与了这些科学家的创造。此外，在《为什么》一书中描述的关键发展，其中包括(1)识别分析，(2)反事实的算法化，(3)中介分析，(4)外部有效性，远远超过2000年前时代的狭隘哲学文献。
佐伊·哈克特在《化学世界》上对《为什么》一书给予了积极的评价，并告诫说，“这需要注意力集中，需要勤奋努力，才能解决书中提出的令人费解的统计问题。”这篇评论的结论是:“他的书对于任何认真学习科学哲学的学生来说都是必读的，而且应该是所有本科一年级统计学课程的必读书目。”
丽莎·r·戈德堡在《美国数学学会公告》上写了一篇详细的技术性评论。
== 参考文献 ==
== 参考文献 ==