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    Description

    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

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    • Global
      3 out of 5 stars
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      5 out of 5 stars
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      3 out of 5 stars
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    • Gary
    • 13/01/2019

    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.

    37 personnes ont trouvé cela utile

    • Global
      4 out of 5 stars
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      3 out of 5 stars
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      4 out of 5 stars
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    • Wingo Smith
    • 05/07/2018

    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.

    25 personnes ont trouvé cela utile

    • Global
      5 out of 5 stars
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      2 out of 5 stars
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      5 out of 5 stars
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    • rrwright
    • 30/05/2018

    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.

    108 personnes ont trouvé cela utile

    • Global
      2 out of 5 stars
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      2 out of 5 stars
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      2 out of 5 stars
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    • 11104
    • 30/07/2018

    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.

    9 personnes ont trouvé cela utile

    • Global
      4 out of 5 stars
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      5 out of 5 stars
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      5 out of 5 stars
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    • jp
    • 28/12/2018

    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.

    6 personnes ont trouvé cela utile

    • Global
      5 out of 5 stars
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      5 out of 5 stars
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      5 out of 5 stars
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    • Jonathan
    • 23/05/2018

    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.

    30 personnes ont trouvé cela utile

    • Global
      2 out of 5 stars
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      4 out of 5 stars
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      1 out of 5 stars
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    • Antonin
    • 15/11/2019

    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...

    4 personnes ont trouvé cela utile

    • Global
      1 out of 5 stars
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      1 out of 5 stars
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      1 out of 5 stars
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    • Sammy
    • 17/08/2019

    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.

    4 personnes ont trouvé cela utile

    • Global
      4 out of 5 stars
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    • Lynde E May
    • 27/12/2018

    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.

    4 personnes ont trouvé cela utile

    • Global
      3 out of 5 stars
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      3 out of 5 stars
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      3 out of 5 stars
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    • Ramos
    • 21/06/2018

    do not recommend this for audio book

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

    8 personnes ont trouvé cela utile

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    • Global
      3 out of 5 stars
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      4 out of 5 stars
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      4 out of 5 stars
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    • Klaus
    • 03/09/2018

    Diagrams

    Good book, but difficult to follow in audio format because of the frequent use of diagrams.

    9 personnes ont trouvé cela utile

    • Global
      3 out of 5 stars
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      5 out of 5 stars
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      2 out of 5 stars
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    • Tatjana
    • 04/09/2018

    Lieber als Buch

    The book is challenging as an audio book. It would be better as Kindle version. You need the PDF anyways.

    8 personnes ont trouvé cela utile

    • Global
      3 out of 5 stars
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      5 out of 5 stars
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    • Utilisateur anonyme
    • 23/12/2018

    I enjoyed it but some problems made may say 3 star

    nice informative book. probably better read than listened to. AI is very briefly discussed. 3

    2 personnes ont trouvé cela utile

    • Global
      3 out of 5 stars
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      5 out of 5 stars
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      4 out of 5 stars
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    • Amazon Customer
    • 13/02/2021

    Important book destroyed by author's arrogance

    This is indeed a very important book, about rigorous and revolutionary work in causality, of which, despite being a mathematician myself, I was totally unaware. The book was also wonderfully narrated but, as other reviewers pointed out, there are a lot of references to images, tables etc which made it very difficult to focus. I ended up listening to it alongside its ebook version, to take the most out of it because I found the content really worth it. However, the arrogance of the author manifesting in every occasion possible, is a truly irreconcilable fact about this book which one should consider beforehand.

    • Global
      4 out of 5 stars
    • Interprétation
      2 out of 5 stars
    • Histoire
      3 out of 5 stars
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    • Boris
    • 12/02/2021

    Worth listening but not the best

    Interesting content. Voice becomes annoying after a few minutes. Would recommend it though for topic lovers.