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Category: Excerpt

《韩非子》里的趣事

零零星星翻译过几个,忘记可以发在这里了。

原文粘贴自「中國哲學書電子化計劃」,白话文是在参考了《读古人书之〈韩非子〉》(邵永海,2017)的译文之后自己改写的。

1

秦王问公子汜要不要跟三国联军割地讲和。公子汜说,反正讲和你也要后悔,不讲和你也要后悔。讲和了,你要说本来不割地人家也会退兵,白割了。不讲和,联军继续攻城略地,你又要说早知道当时讲和了……秦王一想,宁可后悔地白割了,也不能等国家危急了后悔当时没讲和,于是就讲和了。

三國兵至韓,秦王謂樓緩曰:「三國之兵深矣,寡人欲割河東而講,何如?」對曰:「夫割河東,大費也;免國於患,大功也。此父兄之任也,王何不召公子氾而問焉?」王召公子氾而告之,對曰:「講亦悔,不講亦悔。王今割河東而講,三國歸,王必曰:三國固且去矣,吾特以三城送之。不講,三國也入韓,則國必大舉矣,王必大悔,王曰:不獻三城也。臣故曰:王講亦悔,不講亦悔。」王曰:「為我悔也,寧亡三城而悔,無危乃悔。寡人斷講矣。」(《韓非子·內儲說上》)

2

秦相甘茂的手下偷听到秦王对公孙衍说要请他做丞相。甘茂就跑到秦王面前说,恭喜大王找到新丞相了啊。秦王说我已经有你了,哪有什么新丞相。甘茂说,你不是要请公孙衍做丞相吗?秦王问甘茂你怎么知道。甘茂说公孙衍告诉我的啊。秦王觉得公孙衍是个大嘴巴,一怒之下就把他放逐了……

甘茂相秦惠王,惠王愛公孫衍,與之閒有所言,曰:「寡人將相子。」甘茂之吏道穴聞之,以告甘茂,甘茂入見王,曰:「王得賢相,臣敢再拜賀。」王曰:「寡人託國於子,安更得賢相?」對曰:「將相犀首。」王曰:「子安聞之?」對曰:「犀首告臣。」王怒犀首之泄,乃逐之。(《韓非子·外儲說右上》)

3

楚怀王的夫人知道怀王有了新欢,就表现出自己也特别喜欢这位新人的样子。怀王对夫人很赞赏,觉得她是孝子忠臣的榜样。后来,夫人对新欢说,大王虽然很喜欢你,但不太喜欢你的鼻子,你最好经常把鼻子遮住,大王才会更喜欢你。新人按夫人的话照做了。怀王就问夫人,她怎么老是遮着鼻子?夫人说不知道。怀王一再逼问。夫人说,之前听她说讨厌大王的气味。怀王大怒,让人割掉了新人的鼻子……

一曰。魏王遺荊王美人,荊王甚悅之,夫人鄭袖知王悅愛之也,亦悅愛之,甚於王,衣服玩好擇其所欲為之,王曰:「夫人知我愛新人也,其悅愛之甚於寡人,此孝子所以養親,忠臣之所以事君也。」夫人知王之不以己為妒也,因為新人曰:「王甚悅愛子,然惡子之鼻,子見王,常掩鼻,則王長幸子矣。」於是新人從之,每見王,常掩鼻,王謂夫人曰:「新人見寡人常掩鼻何也?」對曰:「不己知也。」王強問之,對曰:「頃嘗言惡聞王臭。」王怒曰:「劓之。」夫人先誡御者曰:「王適有言,必可從命。」御者因揄刀而劓美人。(《韓非子·內儲說下》)

4

子圉将孔子引荐给宋国太宰。孔子离开后,太宰对子圉说:「我见过孔子之后,看你就像看跳蚤虱子,我马上让孔子去见国君。」子圉对太宰说:「国君见了孔子,也会把你当跳蚤虱子的……」于是太宰没有向国君推荐孔子。

子圉見孔子於商太宰,孔子出,子圉入,請問客,太宰曰:「吾已見孔子,則視子猶蚤蝨之細者也。吾今見之於君。」子圉恐孔子貴於君也,因謂太宰曰:「君已見孔子,亦將視子猶蚤蝨也。」太宰因弗復見也。(《韓非子·說林上》)

学术写作指南

上周开始看 Writing Your Journal Article in Twelve Weeks 这本书,有种十年前就该学这些内容的感觉。尤其是这本书里讲的,一定要每天坚持至少十五分钟的写作,不要等,不要等,搞创作要积少成多。

Writing is to academia what sex was to nineteenth-century Vienna: everybody does it and nobody talks about it. A leading scholar of productivity found that most academics were more willing to talk about their most personal problems, including sexual dysfunction, than about problems with writing (Boice 1990, 1). The prevalent belief among academics seems to be that writing, like sex, should come naturally, and should be performed in polite privacy.

Wendy Laura Belcher. 2019. Writing Your Journal Article in Twelve Weeks. p. 15

No matter how busy your life is, make a plan for writing. Successful academic writers don’t wait for inspiration. They don’t wait until the last minute. They don’t wait for big blocks of time. They make a plan for writing five days a week, and they strive to stick to it. Much of this workbook will be devoted to helping you develop writing into a habit. Short and steady sessions will win the race: “With but a few exceptions, writers who remained in a schedule requiring an hour or less a weekday of writing mastered a sequence of strategies for remaining truly productive over long periods of time” (Boice 1990, 3). As an anonymous person wisely commented online, “The only thing that improves writing is writing.”

Wendy Laura Belcher. 2019. Writing Your Journal Article in Twelve Weeks. pp. 18–19

A fur piece = A long distance

刚刚在看 Miller 的 On Literature(中译本《文学死了吗?》——现在听这句话特别像在问候全家),里面说福克纳《八月之光》的开头是:

丽娜坐在路边,看着车上了山朝她开过来。她想,我从阿拉巴马来。一件皮草围巾。一路从阿拉巴马来。一件皮草围巾。

J. Hillis Miller. 2002. On Literature | 文学死了吗. 秦立彦 译 (2007)

我心想什么皮草围巾,并且这两句话还写得这么怪,就去查了一下,两个中译本翻译的都是感慨距离远。

我从亚拉巴马州到了这儿,真够远的。我一路上都是走着来的。好远的一路啊

William Faulkner. 1932. Light in August | 八月之光. 蓝仁哲 译 (2015)

我已经离开了亚拉巴马州,好远。一路从阿拉巴马出发,真远

William Faulkner. 1932. Light in August | 八月之光. 霍彦京 译 (2016)

于是我翻开 Light in August 的原文,对应的原文是 a fur piece。

I have come from Alabama: a fur piece. All the way from Alabama a-walking. A fur piece.

William Faulkner. 1932. Light in August

虽然我也不知道 a fur piece 是 a long distance 的土话(后来 Google 到了),但是我大概也不敢贸然译成皮草围巾,起码我可以查一下现成的中译本吧。

演化视角看伦理学

We have already seen that Sidgwick’s principle of universal benevolence requires us to give no more weight to our own interests than we give to the similar interests of everyone else. Such a principle is unlikely to have been selected for by an evolutionary process; on the contrary, it is exactly the kind of principle that you would expect evolution to select against, because evolution selects for principles that confer advantages on us, our kin, those with whom we are in reciprocally beneficial relationships, and perhaps other members of our small tribe or social group. The need for reciprocity and trust within our social group may well have led to the evolution of a sense of fairness, but the impetus to extend that sense beyond our own group is unlikely to be an evolved automatic response. It is more likely to require the use of our ability to reason. Our reasoning is, of course, a product of evolution, for it enhances our prospects of surviving and reproducing; but it also brings with it the ability to understand things that have nothing to do with evolutionary fitness, such as the ability to do higher mathematics. Perhaps it also brings with it our ability to see that our own interests are no more significant than those of other beings who can enjoy life as much as we can, and can suffer as much as we can. If this is right, the rational basis of Sidgwick’s principle of benevolence is immune from evolutionary debunking arguments, and hence remains standing when these arguments undermine the grounds for accepting non-consequentialist intuitions.

Katarzyna de Lazari-Radek & Peter Singer. 2017. Utilitarianism: A Very Short Introduction

There’s more to life than being happy?

刚在微博上看到严锋老师转发了一场 TED 演讲 There’s more to life than being happy (by Emily Esfahani Smith)。有人总结到「追求快乐让人变得不快乐」,严锋老师说「过份追求快乐让人变得不快乐」。这个话题让我突然有很多话想说。

我们先来看一看 Emily 的开场白:

I used to think the whole purpose of life was pursuing happiness. Everyone said the path to happiness was success, so I searched for that ideal job, that perfect boyfriend, that beautiful apartment. But instead of ever feeling fulfilled, I felt anxious and adrift. And I wasn’t alone; my friends — they struggled with this, too.

我曾经认为生活的全部目的就是追求快乐/幸福(按:happiness 既可以表示短暂的快乐又可以表示较长时段的幸福状态)。每个人都说通往快乐/幸福的道路是成功,所以我追求理想的工作,追求完美的男朋友,追求漂亮的公寓。但是我没有感到满足,我感到的是焦虑和迷茫。不只是我,我的朋友们也有同样的困扰。

后来 Emily 就去学习积极心理学(positive psychology),读了很多心理学、神经科学和哲学的书——恰好也是我最感兴趣的三个领域。她发现,数据显示,追求快乐反而让人不快乐,尽管大家的生活条件越来越好,但抑郁、甚至自杀的人却越来越多。而研究认为这并不是因为他们缺少快乐,而是缺少生活的意义。于是 Emily 开始追问快乐(being happy)和生活的意义(having meaning in life)有什么区别。

Many psychologists define happiness as a state of comfort and ease, feeling good in the moment. Meaning, though, is deeper. The renowned psychologist Martin Seligman says meaning comes from belonging to and serving something beyond yourself and from developing the best within you. Our culture is obsessed with happiness, but I came to see that seeking meaning is the more fulfilling path. And the studies show that people who have meaning in life, they’re more resilient, they do better in school and at work, and they even live longer.

许多心理学家将快乐定义为一种舒适和安逸的状态,也就是在某个时刻感觉很好。而人生的意义是更深一层的概念。著名心理学家马丁·塞利格曼(按:积极心理学之父)说,意义来自归属和服务于超越你自己的事物,来自发展你内心中最好的部分。我们的文化痴迷于追求快乐,但我逐渐认识到寻求人生意义更让人满足。研究表明,有生活意义的人适应能力更强,在学校和工作中表现更好,甚至寿命也更长。

接下来的演讲就是 Emily 分享自己找到的怎样寻求人生意义的四种方式(four pillars of a meaningful life):归属感(belonging)、目的(purpose)、超越(transcendence)、讲故事(storytelling)。Emily 的确讲了很多故事,四种方式各讲了一个故事,讲完还继续讲了自己的故事和父亲的故事。这里就暂且略过。最后她总结到:快乐的感觉有来有去捉摸不定,意义才能让我们掌握人生(Happiness comes and goes. But when life is really good and when things are really bad, having meaning gives you something to hold on to)。

接下来我谈谈感想。

我觉得虽然这是一场 TED 演讲,但其实论证方式特别像心灵鸡汤。我直奔主题,我们思考一下,Emily 的演讲从头到尾证明的是在人生中除了快乐之外还有其他重要之事(there’s more to life than being happy)吗?或者说她证明的是追求快乐反而会让人不快乐吗?

回到 Emily 的开场白,谁说追求快乐就一定等同于追求世俗意义的成功呢?Emily 和她的朋友追求好工作、好伴侣、好房子没有获得满足,最多只能证明这些追求快乐的途径可能有问题,但并不能证明「追求快乐」本身有问题,不能证明「 不应该追求快乐」。Emily 说自己通过追求这些没有获得满足,感到焦虑和迷茫,也就是追求某些目标让人感到不快乐,可以得出的结论应该是:这些具体目标,或者追求这些目标的方式方法可能不太对。也许我们追求另外一些目标更容易获得快乐,也许同样是追求这些目标,但换其他一些追求方式我们更容易获得快乐。你不能把自己原以为的那些目标直接等同于快乐本身,然后一起否定掉。

Emily 随后讲的意义,以及寻求意义的四种方式(归属感、目的、超越、讲故事),其实都可以认为是她找到的另一些追求快乐的方式。撇开她举例论证的效力问题,她可以证明的其实只是某些目标或者某些方式不容易实现快乐,而追求另一些目标或者另一些方法比较容易实现快乐,这并没有贬低快乐本身,也没有必要去抬高一个玄乎的人生意义(积极心理学之父给出的关于意义的定义实在很想吐嘈)。

向外国人介绍金庸

前段时间 Slowly 上的笔友对中国文化感兴趣,但我一时不知道推荐什么资源,总觉得无论是传统文化还是流行文化都很少有现成的资源给外国人看。金庸先生去世之后我才想到,金庸的作品算是现成的资源。YouTube 上也有加英文字幕的电视剧(虽然未必是正版)。今年《射雕英雄传》的英译本 The Legend of the Condor Heroes 也出了第一卷。各大外媒也写了介绍和纪念金庸的文章,顺便在此汇总一下。

The Power of Collider

最近一直在读之前提到的 The Book of Why,我觉得 collider 的概念可能是这本书里最重要的几个概念之一。本来我也可以用自己的语言写一个介绍,但好像懒得动笔,就节选书中几段话放在这里(顺序是我刻意安排的)。

X 与 Y 相关的三种解释:

  1. X 是 Y 的原因;
  2. X 和 Y 有共同的原因;
  3. collider。

We live our lives as if the common cause principle were true. Whenever we see patterns, we look for a causal explanation. In fact, we hunger for an explanation, in terms of stable mechanisms that lie outside the data. The most satisfying kind of explanation is direct causation: X causes Y. When that fails, finding a common cause of X and Y will usually satisfy us. By comparison, colliders are too ethereal to satisfy our causal appetites.

Judea Pearl. 2018. The Book of Why. Chapter 6

什么是 collider?

ABC. This is the most fascinating junction, called a “collider.” Felix Elwert and Chris Winship have illustrated this junction using three features of Hollywood actors: TalentCelebrityBeauty. Here we are asserting that both talent and beauty contribute to an actor’s success, but beauty and talent are completely unrelated to one another in the general population.

We will now see that this collider pattern works in exactly the opposite way from chains or forks when we condition on the variable in the middle. If A and C are independent to begin with, conditioning on B will make them dependent. For example, if we look only at famous actors (in other words, we observe the variable Celebrity = 1), we will see a negative correlation between talent and beauty: finding out that a celebrity is unattractive increases our belief that he or she is talented.

This negative correlation is sometimes called collider bias or the “explain-away” effect. For simplicity, suppose that you don’t need both talent and beauty to be a celebrity; one is sufficient. Then if Celebrity A is a particularly good actor, that “explains away” his success, and he doesn’t need to be any more beautiful than the average person. On the other hand, if Celebrity B is a really bad actor, then the only way to explain his success is his good looks. So, given the outcome Celebrity = 1, talent and beauty are inversely related—even though they are not related in the population as a whole. Even in a more realistic situation, where success is a complicated function of beauty and talent, the explain-away effect will still be present. This example is admittedly somewhat apocryphal, because beauty and talent are hard to measure objectively; nevertheless, collider bias is quite real, and we will see lots of examples in this book.

Judea Pearl. 2018. The Book of Why. Chapter 3

另外两个 collider 的例子:

Try this experiment: Flip two coins simultaneously one hundred times and write down the results only when at least one of them comes up heads. Looking at your table, which will probably contain roughly seventy-five entries, you will see that the outcomes of the two simultaneous coin flips are not independent. Every time Coin 1 landed tails, Coin 2 landed heads. How is this possible? Did the coins somehow communicate with each other at light speed? Of course not. In reality you conditioned on a collider by censoring all the tails-tails outcomes.

Judea Pearl. 2018. The Book of Why. Chapter 6

The correlation we observe is, in the purest and most literal sense, an illusion. Or perhaps even a delusion: that is, an illusion we brought upon ourselves by choosing which events to include in our data set and which to ignore. It is important to realize that we are not always conscious of making this choice, and this is one reason that collider bias can so easily trap the unwary. In the two-coin experiment, the choice was conscious: I told you not to record the trials with two tails. But on plenty of occasions we aren’t aware of making the choice, or the choice is made for us.

The distorting prism of colliders is just as prevalent in everyday life. As Jordan Ellenberg asks in How Not to Be Wrong, have you ever noticed that, among the people you date, the attractive ones tend to be jerks? Instead of constructing elaborate psychosocial theories, consider a simpler explanation. Your choice of people to date depends on two factors: attractiveness and personality. You’ll take a chance on dating a mean attractive person or a nice unattractive person, and certainly a nice attractive person, but not a mean unattractive person. It’s the same as the two-coin example, when you censored tails-tails outcomes. This creates a spurious negative correlation between attractiveness and personality. The sad truth is that unattractive people are just as mean as attractive people—but you’ll never realize it, because you’ll never date somebody who is both mean and unattractive.

Judea Pearl. 2018. The Book of Why. Chapter 6

在控制变量的时候,一定不要控制 collider,因为:

[I]n a collider, ABC, exactly the opposite rules hold. The variables A and C start out independent, so that information about A tells you nothing about C. But if you control for B, then information starts flowing through the “pipe,” due to the explain-away effect.

Judea Pearl. 2018. The Book of Why. Chapter 4

余秋雨最近这几节课讲得很好

第117集 说真话:不用虚假替代真实
第118集 装扮习惯:虚假生态中的文人
第119集 伪精英:空谈是他们唯一的生命方式
第120集 判断真伪文人的基本标准
第121集 面对前辈:不要把尊重变成迷思
第122集 泰斗还是「太逗」:艺术在创新中展开生命力

余秋雨 中国文化必修课

有空我再从中摘选几段贴在这里。

The Science of Well-Being

好几年没有在 Coursera 上听课了。今天是偶然的机会,我在重新听「好和弦」讲流行抒情乐钢琴伴奏之后,又去这个视频里的主唱 JR 的 YouTube 频道看到他在三天前更新的视频介绍了这个耶鲁大学最受欢迎的课程The Science of Well-Being, by Laurie Santos

以下摘自《纽约时报》今年 1 月底的报道:

Students have long requested that Yale offer a course on positive psychology, according to Woo-Kyoung Ahn, director of undergraduate studies in psychology, who said she was “blown away” by Dr. Santos’s proposal for the class.

本科生心理研究主任 Woo-Kyoung Ahn 表示,长期以来,学生们一直要求耶鲁开设一门积极心理学课程。她说,桑托斯博士提出开设这门课程时,她「特别高兴」。

Administrators like Dr. Ahn expected significant enrollment for the class, but none anticipated it to be quite so large. Psychology and the Good Life, with 1,182 undergraduates currently enrolled, stands as the most popular course in Yale’s 316-year history. The previous record-holder — Psychology and the Law — was offered in 1992 and had about 1,050 students, according to Marvin Chun, the Yale College dean. Most large lectures at Yale don’t exceed 600.

安博士等管理人员预计这门课的选修人数会很多,但谁也没预料到会这么多。「心理学与美好生活」这门课目前有 1182 名本科生选修,成为耶鲁大学 316 年历史上最受欢迎的课程。耶鲁学院的院长 Marvin Chun 表示,此前的纪录保持者是 1992 年推出的「心理学和法律」课程,约有 1050 名学生选修。耶鲁的大多数大型课程的选修人数都不超过 600 人。

Yale’s Most Popular Class Ever: Happiness
耶鲁史上最受欢迎课程:快乐

Google 了一下发现这门课已经上线 Coursera(《纽约时报》今年 1 月底报道这门课的时候还只是说很快就会上线)。最近几年觉得国内上 Coursera 的网络状况真的不太好,当然我也不是随时都在测试,毕竟试过几次感觉很糟糕之后就不会太有动力去听课了。但今天的网络效果很好,不知道是不是最近用了另一家代理服务……

我自己对积极心理学(positive psychology)一直比较感兴趣,但也有好几年没有继续阅读这方面的内容了,希望这门课能带给我新的收获吧,积极心理学对个人幸福感的研究在我的哲学里是一块非常重要的基础内容。

附一封 Santos 老师的欢迎信:

Dear Learner,

Congratulations on taking part in this journey! Over the next several weeks, we’ll explore what new results in psychological science teach us about how to be happier, how to feel less stressed, and how to flourish more. We’ll then have a chance to put these scientific findings into practice by building the sorts of habits that will allow us to live a happier and more fulfilling life.

In Spring 2018, I taught “Psychology and the Good Life” for the first time. I created this Yale course because I was worried about the levels of student depression, anxiety, and stress that I was seeing as a Professor and Head of College at Yale. I originally developed this course to teach Yale students how the science of psychology can provide important hints about how to make wiser choices and how to live a life that’s happier and more fulfilling. Since I’m not an expert on positive psychology, I began by learning more about this topic, diving into the work of pioneering scientists like Martin SeligmanEd DienerBarbara FredricksonSonja LyubomirskyMihaly CsikszentmihalyiDaniel GilbertRobert Emmons, and others. I also learned more about work in social psychology and behavior change, including work by scholars such as Liz DunnMike NortonNick EpleyGabriele Oettingen, and others. The Yale course was my attempt at synthesizing work in positive psychology along with the science of behavior change. My goal was to present these scientific findings in a way that made it clear how this science could be applied in people’s daily lives.

When I first developed the class, I had no idea it would become the most popular class ever taught at Yale University. The Yale class was featured in both the national and international news media, and I was flooded with requests from people around the world to find a way to share the content of this Yale class more broadly.

This Coursera class is an attempt to do just that. My goal is to share the insights from that popular Yale class with learners far beyond Yale. To make the lectures feel more intimate, we filmed at my home in one of Yale’s residential colleges with a small group of Yale students in the audience. I hope you’ll enjoy this more personal format, which allows you to hear the sorts of questions Yale students had about the material and how they applied the science in their daily lives. We understand that many of you taking the course are not currently college students, but we hope you see yourselves as though you are part of this virtual classroom.

During this course, you’ll have the opportunity to enhance your own well-being by implementing a few simple research-based methods to your own life.

I am thrilled to share this information with a wider audience. As you go through the lessons please share your feedback with the course team! You can direct item-specific feedback via content flags and general course feedback in the Discussion Forums or in the post-course survey when you complete the course.

Sincerely,
Laurie

Causal Revolution: 描述因果的数学语言

To appreciate the depth of this gap, imagine the difficulties that a scientist would face in trying to express some obvious causal relationships—say, that the barometer reading B tracks the atmospheric pressure P. We can easily write down this relationship in an equation such as B = kP, where k is some constant of proportionality. The rules of algebra now permit us to rewrite this same equation in a wild variety of forms, for example, P = B/k, k = B/P, or B–kP = 0. They all mean the same thing—that if we know any two of the three quantities, the third is determined. None of the letters k, B, or P is in any mathematical way privileged over any of the others. How then can we express our strong conviction that it is the pressure that causes the barometer to change and not the other way around? And if we cannot express even this, how can we hope to express the many other causal convictions that do not have mathematical formulas, such as that the rooster’s crow does not cause the sun to rise?

My college professors could not do it and never complained. I would be willing to bet that none of yours ever did either. We now understand why: never were they shown a mathematical language of causes; nor were they shown its benefits. It is in fact an indictment of science that it has neglected to develop such a language for so many generations. Everyone knows that flipping a switch will cause a light to turn on or off and that a hot, sultry summer afternoon will cause sales to go up at the local ice-cream parlor. Why then have scientists not captured such obvious facts in formulas, as they did with the basic laws of optics, mechanics, or geometry? Why have they allowed these facts to languish in bare intuition, deprived of mathematical tools that have enabled other branches of science to flourish and mature?

Part of the answer is that scientific tools are developed to meet scientific needs. Precisely because we are so good at handling questions about switches, ice cream, and barometers, our need for special mathematical machinery to handle them was not obvious. But as scientific curiosity increased and we began posing causal questions in complex legal, business, medical, and policy-making situations, we found ourselves lacking the tools and principles that mature science should provide.

Belated awakenings of this sort are not uncommon in science. For example, until about four hundred years ago, people were quite happy with their natural ability to manage the uncertainties in daily life, from crossing a street to risking a fistfight. Only after gamblers invented intricate games of chance, sometimes carefully designed to trick us into making bad choices, did mathematicians like Blaise Pascal (1654), Pierre de Fermat (1654), and Christiaan Huygens (1657) find it necessary to develop what we today call probability theory. Likewise, only when insurance organizations demanded accurate estimates of life annuity did mathematicians like Edmond Halley (1693) and Abraham de Moivre (1725) begin looking at mortality tables to calculate life expectancies. Similarly, astronomers’ demands for accurate predictions of celestial motion led Jacob Bernoulli, Pierre-Simon Laplace, and Carl Friedrich Gauss to develop a theory of errors to help us extract signals from noise. These methods were all predecessors of today’s statistics.

Ironically, the need for a theory of causation began to surface at the same time that statistics came into being. In fact, modern statistics hatched from the causal questions that Galton and Pearson asked about heredity and their ingenious attempts to answer them using cross-generational data. Unfortunately, they failed in this endeavor, and rather than pause to ask why, they declared those questions off limits and turned to developing a thriving, causality-free enterprise called statistics.

This was a critical moment in the history of science. The opportunity to equip causal questions with a language of their own came very close to being realized but was squandered. In the following years, these questions were declared unscientific and went underground. Despite heroic efforts by the geneticist Sewall Wright (1889–1988), causal vocabulary was virtually prohibited for more than half a century. And when you prohibit speech, you prohibit thought and stifle principles, methods, and tools.

Readers do not have to be scientists to witness this prohibition. In Statistics 101, every student learns to chant, “Correlation is not causation.” With good reason! The rooster’s crow is highly correlated with the sunrise; yet it does not cause the sunrise.

Unfortunately, statistics has fetishized this commonsense observation. It tells us that correlation is not causation, but it does not tell us what causation is. In vain will you search the index of a statistics textbook for an entry on “cause.” Students are not allowed to say that X is the cause of Y—only that X and Y are “related” or “associated.”

… I hope with this book to convince you that data are profoundly dumb. Data can tell you that the people who took a medicine recovered faster than those who did not take it, but they can’t tell you why. Maybe those who took the medicine did so because they could afford it and would have recovered just as fast without it.

Over and over again, in science and in business, we see situations where mere data aren’t enough. Most big-data enthusiasts, while somewhat aware of these limitations, continue the chase after data-centric intelligence, as if we were still in the Prohibition era.

As I mentioned earlier, things have changed dramatically in the past three decades. Nowadays, thanks to carefully crafted causal models, contemporary scientists can address problems that would have once been considered unsolvable or even beyond the pale of scientific inquiry. For example, only a hundred years ago, the question of whether cigarette smoking causes a health hazard would have been considered unscientific. The mere mention of the words “cause” or “effect” would create a storm of objections in any reputable statistical journal.

Even two decades ago, asking a statistician a question like “Was it the aspirin that stopped my headache?” would have been like asking if he believed in voodoo. To quote an esteemed colleague of mine, it would be “more of a cocktail conversation topic than a scientific inquiry.” But today, epidemiologists, social scientists, computer scientists, and at least some enlightened economists and statisticians pose such questions routinely and answer them with mathematical precision. To me, this change is nothing short of a revolution. I dare to call it the Causal Revolution, a scientific shakeup that embraces rather than denies our innate cognitive gift of understanding cause and effect.

Side by side with this diagrammatic “language of knowledge,” we also have a symbolic “language of queries” to express the questions we want answers to. For example, if we are interested in the effect of a drug (D) on lifespan (L), then our query might be written symbolically as: P(L|do(D)). In other words, what is the probability (P) that a typical patient would survive L years if made to take the drug? This question describes what epidemiologists would call an intervention or a treatment and corresponds to what we measure in a clinical trial. In many cases we may also wish to compare P(L|do(D)) with P(L |do(not-D)); the latter describes patients denied treatment, also called the “control” patients. The do-operator signifies that we are dealing with an intervention rather than a passive observation; classical statistics has nothing remotely similar to this operator.

We must invoke an intervention operator do(D) to ensure that the observed change in Lifespan L is due to the drug itself and is not confounded with other factors that tend to shorten or lengthen life. If, instead of intervening, we let the patient himself decide whether to take the drug, those other factors might influence his decision, and lifespan differences between taking and not taking the drug would no longer be solely due to the drug. For example, suppose only those who were terminally ill took the drug. Such persons would surely differ from those who did not take the drug, and a comparison of the two groups would reflect differences in the severity of their disease rather than the effect of the drug. By contrast, forcing patients to take or refrain from taking the drug, regardless of preconditions, would wash away preexisting differences and provide a valid comparison.

Mathematically, we write the observed frequency of Lifespan L among patients who voluntarily take the drug as P(L|D), which is the standard conditional probability used in statistical textbooks. This expression stands for the probability (P) of Lifespan L conditional on seeing the patient take Drug D. Note that P(L|D) may be totally different from P(L|do(D)). This difference between seeing and doing is fundamental and explains why we do not regard the falling barometer to be a cause of the coming storm. Seeing the barometer fall increases the probability of the storm, while forcing it to fall does not affect this probability.

Judea Pearl. 2018. The Book of Why