最初の1冊は無料。今すぐ聴こう。

30日間の無料体験後は月額1,500円。いつでも退会できます。

OR
カートに追加済み

このタイトルを購入されたお客様はこちらも購入されています...

『Noise』のカバーアート
『The Master Algorithm』のカバーアート
『How to Avoid a Climate Disaster』のカバーアート
『Factfulness』のカバーアート
『Thinking, Fast and Slow』のカバーアート
『Why We Sleep』のカバーアート

あらすじ・解説

How the study of causality revolutionized science and the world

"Correlation does not imply causation". This mantra has been invoked by scientists for decades and has led to a virtual prohibition on causal talk. But today, that taboo is dead. The causal revolution, sparked by Judea Pearl and his colleagues, has cut through a century of confusion and placed causality - the study of cause and effect - on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet, and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: It lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

PLEASE NOTE: When you purchase this title, the accompanying reference material will be available in your Library section along with the audio. 
©2018 Judea Pearl and Dana Mackenzie (P)2018 Brilliance Publishing, Inc., all rights reserved

同じ著者・ナレーターの作品

ナレーター

The Book of Whyに寄せられたリスナーの声

カスタマーレビュー：以下のタブを選択することで、他のサイトのレビューをご覧になれます。

Audible.co.jp

Audible.com

Audible.co.uk

Audible.com.au

Amazon.co.jp
レビューはまだありません。
並べ替え：
絞り込み：
  • 総合評価
    3 out of 5 stars
  • ナレーション
    5 out of 5 stars
  • ストーリー
    3 out of 5 stars
  • Gary
  • 2019/01/13

Understand the data, understand the world

There were some real flaws with this book that bothered me to no end. I had no problem following his statistical examples and how to think about data analysis in the way the author suggests we all should. I even enjoyed it when the author connected what he called Smart Artificial Intelligence to his overall causal theory, and I enjoyed the book when he alluded in passing to the importance of solving the P=NP problem and how that would relate to what most people call super AI .

If we can model completely the effect of a variable, we can get beyond the functional representation and understand the cause, that is, the why, the intentional or the intuition lying behind the variable of interest. Or in other words, as the author wants to say correlation can show causation if a perfect model is in place for the variable under consideration. The author is absolutely right, but perfect unique models of the real world don’t exist.

The more we understand about the process, the interactions, the confounding and the mediated variables the better our model will be and every statistician tries to do just that with every dataset they come across. All statisticians want to understand the process, but in reality often they have to let the data lead the way.

Good data analysts (statisticians) know that if they control for mediated variable that collude with resultant variables they risk confounding. While everyone might not understand what those words mean in a strict statistical sense, I would think that everyone who thinks about the world through the lens of modeling and data knows confounding can be lurking. The author is not really telling us anything that most people didn’t already know, at least among people who analyze the world with data.

Science never proves anything. We say things are a fact, but when we say something is a ‘fact’ we really imply ‘scientific fact’. The world of facts is always ‘underdetermined’. That means that the facts we have can always be explained by multiple theories. The author is talking about data, the facts that make up the world. He wants to show that correlation can show causation when we see beyond the data and wants us to consider graphical analysis and use his ‘do calculus’ and causal path analysis as tools for developing a model. (I owe a slight elaboration to why I say ‘science never proves anything’, at least that is the standard paradigm that science uses with its null hypothesis and alternative hypothesis. We reject the null and accept the alternative at the level of six sigma in quantum physics or 2 sigma in psychology. That is what scientists do and yes it does come from R. A. Fisher who the author mentions multiple times mostly in order to criticize).

The author is right to say that a statistician needs to understand the processes and the underlying mechanisms at play within the issue under study, but every statistician already knows that. The more the analyst understands about the process the stronger the statement can be made. ‘Climate change is a (scientific) fact’. We can say that not just because of the data, but because of the well understood climate models that have been fine tuned over time that mirrors our data expectations post-prediction and retro-diction (you know, Einstein’s heart skipped a beat when he saw that the perihelion of Venus fit his General Theory even though that was retro-diction, after the fact).

The statistician will perform sensitivity analysis, model fitting, Bayesian analysis (taking prior information and using that to get a result and weighing those results by expectations), graphing, identifying the mediated variables, perform ceteris Paribas, contra-factual analysis or in general anything that reasonably needs to be done in order to confirm or deny their best alternative hypothesis and all of those techniques are prominently featured in this book.

The author likes Harari’s first book “Sapiens” because it fits the story he is telling. He quotes Harari to the effect that humans are different because we are the only creature who knowingly believe a fiction. The author doesn’t quote Harari’s second book ‘Homo Deus’. In that book Harari will say that big data analysis will understand our causes from the data itself, the ‘whys’ of whom we are, or in other words our intentional state beyond our functional state, or in other words our intuitions beyond the action itself. The author thinks more than data itself is a requirement, Harari, at least in his second book, will say big data itself will be sufficient with the right self adjusting programming.

I don’t dislike this book at all. I liked how the author does talk intelligently about super AI on the peripheral; I like that the author has reasonable ways to think about complex problems. I felt that most analysts would understand the points the author was making, and I felt the author ignored too much of the Philosophy of Science in his presentation, and I think most people already know that data analysis and process analysis always must go hand in hand, but contrary to what the author is saying I think that sometimes one must let the data do the explaining for the analyst, and we must always remember that certainty is always illusive in the real world.

  • 総合評価
    4 out of 5 stars
  • ナレーション
    3 out of 5 stars
  • ストーリー
    4 out of 5 stars
  • Wingo Smith
  • 2018/07/05

Great material, but highly visual

The examples, problems (and theirsolutions), etc. rely on visual models. You can look at the PDF of the diagrams while listening, but it’s tough to do if, like me, you mostly listen during times when visuals are inconvenient, say on your commute to work or while running,

The book is super interesting, though. Well worth a listen with pdf in hand.

  • 総合評価
    5 out of 5 stars
  • ナレーション
    2 out of 5 stars
  • ストーリー
    5 out of 5 stars
  • rrwright
  • 2018/05/30

Great book! Not a great audiobook.

This is a book about probability and graphical models. As an avid audiobook reader, it pains me to say: this is not a good audiobook. There are quite a lot of figures that are essential to understand the text (e.g. textual references to “A” and “B” in figure X.X), and many probability formulae that are read out loud in all their manifold subscript glory. This makes the text very hard to understand from the audio alone (and there was no accompanying PDF to show the figures when I bought this book!).

But this is a fantastic book! I bought the kindle version to complement the audio. I would suggest that this is essential. Even if the figures accompanied the text in a PDF, the formulae in the text still make the audio an insufficient medium to comprehend the rich ideas expressed.

I’m very glad this audiobook exists; it gives me a way to chew on many of these ideas when I can’t read a physical book. But you should definitely expect to read the textual version if you want a good understanding of this material.

  • 総合評価
    2 out of 5 stars
  • ナレーション
    2 out of 5 stars
  • ストーリー
    2 out of 5 stars
  • 11104
  • 2018/07/30

Couldn't make it past an hour

The subject matter is interesting but this is a bad audio book.The narrator is terrible. His delivery is sing-song and didactic with no emotional nuance to follow the text. It was as if he were reading a difficult story to children. I gave the narrator two stars because his diction is excellent but I couldn't stand to listen to him any more.

I can't say much about the content because I didn't make it very far into the book. The ideas are quite abstract and difficult to absorb in an audio version. The book depends on many illustrations and diagrams. You can download these but I listen to audio books while driving so they are of no use to me. During the time I spent with the work, I could not decide whether it describes new tools for statistical analysis or is a bit crackpot.

  • 総合評価
    4 out of 5 stars
  • ナレーション
    5 out of 5 stars
  • ストーリー
    5 out of 5 stars
  • jp
  • 2018/12/28

Great book, but hard to get the details over audio

Great book. Clearly written with great examples. Some parts discussing math or graphs are difficult to grasp in audio form, however.

  • 総合評価
    5 out of 5 stars
  • ナレーション
    5 out of 5 stars
  • ストーリー
    5 out of 5 stars
  • Jonathan
  • 2018/05/23

The accompanying pdf is missing

The book is meant to come with a pdf but it hasn't been attached. I like the book though.

  • 総合評価
    2 out of 5 stars
  • ナレーション
    4 out of 5 stars
  • ストーリー
    1 out of 5 stars
  • Antonin
  • 2019/11/15

Unnecessarily missleading

The theory is beautiful. Causality is needed and useful. The author although, is self centered to the extend of narcissism. I did not enjoy the quotes from The Old Testament, even less so in different language than English. I lost all interest after he calls himself a giant...

  • 総合評価
    1 out of 5 stars
  • ナレーション
    1 out of 5 stars
  • ストーリー
    1 out of 5 stars
  • Sammy
  • 2019/08/17

Difficult

When I heard the book rivaled Kahneman Fast and Slow I bought. Not even close. You can’t just listen. Too many equations, graphs and complex statistics. Then the self aggrandizing attitude and ad hominem attacks on prior statisticians is a complete turnoff.

  • 総合評価
    4 out of 5 stars
  • Lynde E May
  • 2018/12/27

Awesome...

...but probably better read than listened to. there are a lot of formulas that aren't that technical but easier if you can see them as you read.

  • 総合評価
    3 out of 5 stars
  • ナレーション
    3 out of 5 stars
  • ストーリー
    3 out of 5 stars
  • Ramos
  • 2018/06/21

do not recommend this for audio book

too much mathematicians story, many opinions and not concise. may be better for a book

並べ替え：
絞り込み：
  • 総合評価
    2 out of 5 stars
  • ナレーション
    1 out of 5 stars
  • ストーリー
    2 out of 5 stars
  • Nigel Warburton
  • 2019/09/06

Is the narrator a robot?

Such a bad American voice, nice gateway book, although quite convoluted despite the chapter areas supposedly breaking it down. It doesn't transcend the sum of its parts into 'a book' for me but resembles more a collection of essays bound in a book format. it might well have just given the equations and formal diagrams and be done in a chapter rather than go on repeating cliches or the most boring anecdotes.

Moreover, the thesis of a new science where for decades science was in statistical stasis makes so little sense. On the one hand he says that causation is intuitive and precedes all other scientific endeavours but then on the other that people gathered data for association and nothing else for decades! Lacking an AI vocabulary doesn't mean causation didn't exist as a basis for pretty much all natural sciences (precluding pure mathematics).

Read David Deutsch on AI's toddling progress, also, it doesn't deliver what it promises. Made me sleepy not because of content but because it just takes so long to get to the point which is usually merely that drawing causation as a diagram is better than not.

What is most annoying, however, is the narrator. Staid American accents impair my ability to take in the information from so many good books like this, Book of Why, Steven Pinker written titles, The Secret of Our Success and so on. The way these narrators narrate it's as though they *are* a program which just reads out in a level voice without any understanding of the topic. Any other voice would have been better. Why do publishers inflict this in audio? Especially on an audience like the UK, why not Jonathan Kebble, Simon Vance, Stephen Fry, Jeremy Irons, Kate Winslet, Emma Thompson, Peter Noble, Ric Jerom or any actor able to emote and speak in a clear variable intonation for a British audience?

  • 総合評価
    2 out of 5 stars
  • Amazon Customer
  • 2019/09/06

Should not have been an audiobook

Interesting book, however this should never have been an audiobook, to many formulas and references to figures.

  • 総合評価
    5 out of 5 stars
  • ナレーション
    5 out of 5 stars
  • ストーリー
    5 out of 5 stars
  • Mr. A. B.
  • 2018/08/10

Read it today

This book will cause you to think, and think deep. The content of this book should be made available to schools for people to have access to these concepts from younger age but of course they should be adopted for early thinking in one's life.

This book talks about concepts that are hard to get in from the outset and definitely it is a book that needs to be read alongside listening; if that's what you're like but to get full access to the knowledge it consists of you definitely need to study it slowly and think about the equations and theories so that you help yourself gain access to the useful application thereof.

  • 総合評価
    1 out of 5 stars
  • ナレーション
    1 out of 5 stars
  • ストーリー
    5 out of 5 stars
  • Beany
  • 2020/08/14

Get the text book not the audio

struggled to pay attention to narrator. sleepy and dull, slow and unmodulated. felt like listening to a really dull old college professor, even though I know from reading the book that the content is extremely interesting. waste of an audiobook.

  • 総合評価
    3 out of 5 stars
  • ナレーション
    4 out of 5 stars
  • ストーリー
    4 out of 5 stars
  • kunal haria
  • 2020/05/23

no pdf means graphs described unseen

liked the book overall.. have recommended it to friends.. but as an audiobook.. no pdf ?

  • 総合評価
    4 out of 5 stars
  • ナレーション
    5 out of 5 stars
  • ストーリー
    4 out of 5 stars
  • Olly Buxton
  • 2019/09/25

Starts out well but format is a challenge.

this is a fascinating book but not ultimately very well suited to the audiobook format, as there is much discussion of diagrams and quite a lot of discussion of algebraic formulation. Judea Pearl is the leading scientists in causal theory field, and while it is a valiant attempt to write a a general interest book for non specialists, ultimately he gets so far into the technical details that is almost impossible to follow, at least on an audiobook. There are only so many times one can listen to sentences like "the P of Ý given du X is estimable subject to a du operator".

並べ替え：
絞り込み：
  • 総合評価
    5 out of 5 stars
  • ナレーション
    5 out of 5 stars
  • ストーリー
    5 out of 5 stars
  • 匿名
  • 2018/07/09

Amazing book

I was always intrigued by AI and searched for foundational information on how an AI can be built ? Stumbled across this book which is quite frankly tackles the fundamental building block of AI
Computing to reasoning !
We are a long way off from achieving that
Excellent book!!! Loved every bit of it may be will get a hard copy for my collection