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The Book of Why: The New Science of Cause and Effect

3.96  ·  Rating details ·  1,764 ratings  ·  237 reviews
'Correlation does not imply causation.' This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smoking and cancer and carbon dioxide and global warming. But today, that taboo is dead. The causal revolution, sparked by world-renowned computer scientist Judea Pearl and his colleagues, has cut ...more
Kindle Edition, 432 pages
Published May 1st 2018 by Penguin (first published 2018)
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Andrew Harlan There's little about randomized controlled trials in it. The book is about trying to infer causation from non-experimental data.
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Sep 03, 2018 rated it did not like it  ·  review of another edition
I had high hopes for this book. I've been interested in causal inference for a number of years, and I think it's an field that could drastically improve the practice of statistical science if its techniques became widely adopted. A popular book on the field, written by one of its founders, seemed like an exciting development. Finally, people would be talking about this stuff! It would no longer be just another arcane heterodoxy, invoked by academic gadflies in seminar rooms and on blogs, ...more
Andrew Harlan
Failed revolution

In an old joke, an engineer, a physicist and an economist are marooned on a desert island with canned food. They are trying to figure out the best way to open the cans, and while the engineer and the physicist propose various mechanical schemes to get the job done, the economist says, "Let's assume we have a can opener..." Judea Pearl's approach to causal inference brings that joke to mind. His causal calculus begins with the premise, "Let's assume we have a strong causal
Alex Telfar
May 21, 2018 rated it really liked it  ·  review of another edition
I enjoyed this book! It did everything a good book should do, it provides; understandable examples, entertaining side-notes, applications to the real world, something useful that is novel/little known.

The book could have been better (5 stars) if it was more concise, explained the general algorithms for; mediation analysis, independence testing, transfer, ... explained the relationship of causal inference to calculus and spent less time on its whig history and adversarial narrative.

I think
Terran M
May 24, 2018 rated it it was ok  ·  review of another edition
I've never met Pearl, but having read a couple of his books, I'm pretty sure he's an asshole. His anger and bitterness comes through very clearly in his book — he spends as much space naming and vilifying his professional enemies, both living and dead, as he does explaining his work. This is a real shame, because his work is actually quite good and deserves a popular presentation; sadly the sanctimony in this book is almost unbearable, and there is no humor to lighten it.

Unfortunately I don't
Gary  Beauregard Bottomley
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
Jan 19, 2019 rated it did not like it  ·  review of another edition
This review is for the audio version. This topic is very interesting but audio is a terrible format for this book. The narrator is reading out equations. The whole point of the book is to use diagrams. There is a PDF with the audiobook, but the figures are not meaningful on their own. I have ordered the print version. If that makes sense, I will bump up the rating for that.

There were important things I just didn’t understand clearly. For example, he seems to be bashing Sir Austin Bradford Hill,
Thiago Marzagão
This is an engaging, well articulated discussion of causal inference - what it is, what the available tools are (RCTs, IVs, matching, etc), how they have changed over the years, and how they could be improved. The bits that tell the history of causal inference are especially illuminating; I learned a lot of stats in grad school but very little about the struggles and accidents that produced the tools I learned. Pearl helps put much of that into context.

Now, Pearl's intended audience is clearly
Jun 11, 2018 rated it really liked it  ·  review of another edition
Shelves: new-sciences
Here is an excellent book by a renowned expert but potentially with deep fundamental flaws and conclusions. The reviewer is more likely mistaken in these views given that the author is clearly a master thinker on the subject - a point worth noting for any soul wading through this long review. Much of this review is hardly a book review as it is more about the argument gaps this reviewer sees in the author’s epistemological framework rather than the book itself.

Before my disagreements with some
Ryan Sloan
There are great ideas in this book. I'm not an expert on causality or statistics, but I found the idea of modeling causality using a directed graph, and using that graph as a tool for both a) determining valid controls in experimental data and b) performing counterfactual reasoning to be thoughtful and (probably) useful. I have little doubt that Pearl's contributions to the sciences will prove to be important and useful. But I have a hard time endorsing this book. I recognize I'm in the minority ...more
Athan Tolis
Oct 08, 2018 rated it it was amazing  ·  review of another edition
My son George’s first language is Japanese.

His first annoying habit, which raised its head very soon after he was granted the gift of speech, was to answer every request / question / casual comment with “doshte?”

“Doshte,” you guessed it, is Japanese for “why?”

This, Judea Pearl argues very persuasively in this book, is –for the time being-- the biggest difference between thinking men and thinking machines.

I LOVED this book. Loved it, loved it, loved it.

You can read “The Book of Why?” as a
Kelly Jade
The book would have been 100 pages shorter if the author spent less time name dropping and talking himself up.

We get it.

Everyone who opposes you is wrong and stupid and you're the greatest and smartest, just look at all your students with all these high level faculty positions.

Interesting ideas but a lot of ideas could have been explained more clearly or completely if the author laid off the commentary and ego stroking.
Daniel Christensen
If you are a science or stats geek, or frustrated with the replication crisis in across various disciplines, or even a philosophy/ cognitive science boffin, this book is highly recommended.

Judea Pearl is a heavy heavy hitter. He was a big deal in Computing and Artificial Intelligence (at the forefront of Bayesian networks, which are central to mobile phone signal technology), before he made the leap to questions of causal inference.

The knock on Pearl has been his writing – it’s so hard to get
Emre Sevinç
Nov 18, 2018 rated it it was amazing  ·  review of another edition
If I've earned a penny every time I heard the sentence "correlation is not causation", I'd be a richer man by now, and that'd probably be a causal relationship.

If correlation is not causation, then what is causation? I, like many others, asked this question since I took my first undergraduate courses in probability and statistics back in 1990s. I, like many other curious souls, couldn't see a strong, rigorous mathematical answer, and later in life, at least for me, the topic of counterfactuals
Karel Baloun
Nov 05, 2018 rated it it was amazing  ·  review of another edition
Valuable for your permanent for ongoing reference and inspirational revisiting, with an absolutely ideal annotated bibliography. Artisan crafting to certainly withstand the test of time.

Invest 2-3 days in simplifying and repairing how you think causally! I’m sure glad I did. Fun and readable, and so practically valuable. The brilliant core tenet: data are dumb, people’s models can be smart. Knowledge is in the model, not just waiting to emerge from the data. Wow.. that contradicts completely
Jan 12, 2019 rated it it was ok  ·  review of another edition
The examples were good, but for the rest of it the writing was muddled. Plus, I chose to listen to this as an audio book which was a huge mistake because you can't see any of the diagrams and this book seems to rely on them. I think his theory is interesting, but I wouldn't recommend this book.
José María
Oct 22, 2019 rated it it was amazing  ·  review of another edition
I wish I had known this stuff when I was doing my PhD.
Peter McCluskey
Jul 17, 2018 rated it really liked it  ·  review of another edition
This book aims to turn the ideas from Pearl's seminal Causality into something that's readable by a fairly wide audience.

It is somewhat successful. Most of the book is pretty readable, but parts of it still read like they were written for mathematicians.

History of science
A fair amount of the book covers the era (most of the 20th century) when statisticians and scientists mostly rejected causality as an appropriate subject for science. They mostly observed correlations, and carefully repeated the
Jason Furman
This was a long, strange trip through the statistical analysis of causation. Judea Pearl writes beautifully and in an almost grandiose manner, dubbing himself a Whig historian of the science of causation--how it was forgotten by statistical analysis that put correlation at the pinnacle of analysis, how it was rediscovered later, and in particular the importance of structural models that combine an understanding of the world with the data--but do not just let the data speak for itself. The book ...more
Jayson Virissimo
I was expecting something at about the level of Thinking: Fast and Slow, but this book is far more technical, and much of it went over my head.

I’ll have to revisit this after reviewing stats and actually learning some graph theory before giving this a meaningful rating.
Siddhartha Banerjee
Every now and then you read a book that introduces you to a new concept and forces you to reevaluate your world view, leaving you better for it. For me, this was one such book. Highly, highly recommend.
How do you create a robot or computer that can pass the Turing test? Where it can converse well enough to fool a human into think that it too is human? Pearl argues that it is our ability to find causality that is the secret. He recounts his and others research into solving for causality, and the history of the discipline.

Why I started this book: On the updated navy Professional Reading list and it had an audio copy.

Why I finished it: Fascinating theory, heavy on the math and self-promotion.
Rif A. Saurous
Jun 21, 2018 rated it really liked it  ·  review of another edition
This is a popular science intro to causality from Judea Pearl (cowritten with a Dana Mackenzie, a science writer, who probably did most of the writing, although the book is told in Pearl's "voice"). Judea Pearl is an absolute titan of computer science and machine learning, being more-or-less responsible for both probabilistic graphical models and the modern approach to causality. In this book, he attempts to explain "the causal revolution."

I found the book mostly readable, but I question whether
Siddarth Gore
Have you ever noticed that, among the people you date, the attractive ones tend to be jerks?

The book presents a new way of looking at how we program computers and also a new tool to do statistical modelling in general. The theory is indeed very interesting and surprisingly simple to understand. Self-evident if you will. But then most great theories seem that way once someone has come up with them in the first place.

But the book is too damn boring. I would suggest read the first and the last
Richard Thompson
Jun 27, 2018 rated it really liked it  ·  review of another edition
Shelves: philosophy
My mistake with this book was to listen to it as an audio book. I'm sure that it would have been better if I had listened with the PDF diagrams at my side, but Mr. Smarty Pants thought he could absorb it all in his car. Not. I certainly got the general drift and understood the concepts behind the back door method, the front door method and dealing with mediators, but a lot of the richness of the illustrations was lost on me without the ability to work directly with the causal diagrams that are ...more
Carl Zimmer
Jul 05, 2018 rated it really liked it  ·  review of another edition
Cause and effect may seem like the stuff of pure philosophy, but Judea Pearl shows how important causation is to the applications of science, from the technology in our cell phones to the link from smoking to cancer. Pearl, a UCLA computer scientist, presents a personal history of this field using lots of light-hearted thought experiments to illustrate his points. It requires serious concentration, but that concentration is amply rewarded.
George Berry
Jul 07, 2018 rated it really liked it  ·  review of another edition
This is a well written and accessible introduction to Pearl's ideas on causality. It's easier to remember new ideas when they come with vivid illustrations, rather than the drier mathematical presentation in "Causality".

While this book is marketed to a more-or-less mass audience, it contains a few equations and some dense passages.
Jeethu Rao
Mar 19, 2019 rated it it was amazing  ·  review of another edition
The book of why

“Correlation does not imply causation” is the oft cited maxim in statistics. This book begins with the question, ‘If correlation does not imply causation, then what does?’ and then proceeds to introduce the nature of causality and causal inference. The author takes occasional segues to gibe at figure heads and practitioners of classical statistics which traditionally swept the question of causality under the rug except in the case of RCTs. The gold standard for determining the
Nikolaos Korasidis
What a brilliant, brilliant book! This is the most audacious textbook of popularized science I've read in a while and it was nothing short of a complete revelation.

If you are lucky enough to only have an elementary background on statistics, then this book is for you: it will explain what statistics is meant to accomplish, namely, illuminating causal relations. While the subject matter may at times seem a bit foreign, the book's story-driven exposition should convince any newbie how much abstract
Jane Robertson
Sep 23, 2019 rated it did not like it  ·  review of another edition
Giving this book only one star although that's more a reflection of my ability to understand the material. I must learn more about statistics, probabality, causality, bayesian networks, big data methods and limitations, AI. I may have to buy this book for the bibliography.

My one big takeaway from this book is that there's a huge gap in how data is analyzed with statistics. There's so much data but analyzing it is incredibly complex. Humans look for cause and causal pathways. But causality is not
Tõnu Vahtra
Mar 10, 2019 rated it really liked it  ·  review of another edition
Beyond big data... one of those books that you should not be taking up as audiobook, the mathematical discussions were difficult to follow at times to say the least. Though the calculus is probably too much for everyday life/work anyway, on a practical level familiarity with those concepts helps to acknowledge and avoid cognitive fallacies. The biggest case of the book is probably about modelling various interventions and how causal models will take you through when raw statistics falls short ...more
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“If I could sum up the message of this book in one pithy phrase, it would be that you are smarter than your data. Data do not understand causes and effects; humans do.” 4 likes
“You cannot answer a question that you cannot ask, and you cannot ask a question that you have no words for.” 3 likes
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