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

3.94  ·  Rating details ·  3,435 ratings  ·  442 reviews
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence

"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Jud
Hardcover, 432 pages
Published May 15th 2018 by Basic Books (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.…moreThere's little about randomized controlled trials in it. The book is about trying to infer causation from non-experimental data.(less)

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Sep 03, 2018 rated it did not like it
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, generat ...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 theory
May 22, 2021 rated it it was amazing
Recommends it for: Anyone who's ever wondered about the relationship between causality and correlation
Well, I am not an expert on statistics, so maybe I'm missing something important, but I really don't understand all the negative criticism that I see in other reviews of this book. Pearl, who has spent a long career working in an area which spans statistical reasoning, philosophy and AI, set himself an extremely ambitious goal: he wanted to establish a clear, logically consistent foundation for the notions of causality ("A makes B happen") and counterfactuals ("B would have happened if A had hap ...more
Terran M
May 24, 2018 rated it it was ok
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 ha
Alex Telfar
May 21, 2018 rated it really liked it
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 Judea
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 co
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
Jun 11, 2018 rated it really liked it
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 o
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,
Emre Sevinç
Nov 18, 2018 rated it it was amazing
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 s
Thiago Marzagão
Jun 06, 2018 rated it it was amazing
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 t
Athan Tolis
Oct 08, 2018 rated it it was amazing
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 popul
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 t
Kelly Jade
Dec 04, 2018 rated it it was ok
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.
Peter McCluskey
Jul 17, 2018 rated it really liked it
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
Aug 29, 2020 rated it really liked it
Shelves: non-fiction
This was a borrowed book, the kind of books for which I have the utmost respect. Meaning, no reading it in the beach or anywhere close wet things. Which is why it took longer than expected, although finally I had to disrespect it just a little tiny bit since I had a deadline to return it.
Do borrowed books take less to be read than any other kind of books? We might find a correlation between these two variables. Correlation is not causality, however (and I'm not entirely sure there's such correla
Feb 13, 2021 rated it really liked it  ·  review of another edition
a very insightful and revolutionising view into the causal revolution. it's clear that using the author's remarks, one can finally start finding cause in the world, instead of being lost in meaningless correlations. the book is for someone deeply interested in the future of AI, in the future of causal research in medicine, psychology, and similar science fields. a casual reader will have difficulties following the more complicated concepts as the book often resembles a textbook rather than a pop ...more
Siddhartha Banerjee
Aug 09, 2018 rated it it was amazing
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.
José María
Oct 22, 2019 rated it it was amazing
I wish I had known this stuff when I was doing my PhD.
Karel Baloun
Nov 05, 2018 rated it it was amazing
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 wha
Alex Lee
This is amazing. Essentially Pearl and Mackenzie provide a manner to assess causation through data alone.

The key is to provide a model for causation to test the data against. Much of the stats goes over my head, but intuitively we understand how to test for causation; how to get at what matters, what doesn't, what kind of matters and under what conditions we should experiment.

But then again, we don't. Often we control for too much, indirectly influencing our experiments. What we have here is a f
Feb 03, 2021 rated it it was ok
Shelves: cs, popular-science
I love such books, and I wanted to love this one as well. However, this was not for my taste. I assume I wanted to see more reference to AI than causation theory. And I believe the authors extended every chapter more than necessary. Therefore, after a point, I got bored.
Jan 12, 2019 rated it it was ok
Shelves: borrowed-book
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. ...more
Irma Ravkic
Apr 04, 2020 rated it it was amazing
The book examines the notion of causality (the question of why something happened) and why it's still something we don't excel in both in our lives and science. Judea Pearl is a well-known computer scientist who invented Bayesian networks (that are not necessarily causal), and in this book, he discusses that in order to have strong AI (many people believe we already have it), we need our AI machines to have some notion of causality and being able to deal with counterfactuals (I have done X and I ...more
May 22, 2020 rated it really liked it
It took me an incredibly long time to finish this book, but in the end, after a global pandemic caused me to return to my half-finished book pile, I did really like it. Maybe more like 3.5 stars but I'll round up. What I like the most about this is the clear-minded ideas on formalizing assumptions about a causal model before fitting to data (using a graph, in Pearl's view) and then testing those assumptions. In hindsight, this approach with the graphs would have improved some of the work I've do ...more
Jason Furman
Aug 16, 2018 rated it it was amazing
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 c ...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.
A very clearly written popular-statistics/AI book. Definitely makes me want to study the causal analysis formally.

While reading this book, I often thought to myself "it'd be better if I had read this in my (under)graduate school." (But yeah, this book had not been written 10 years ago.) Or in general, I wish myself having a better statistic education so that I could be a lot more careful in designing experiments and interpret data. Unfortunately, most of the programs other than statistics-rela
Aug 14, 2018 rated it really liked it
Mind over matter. Metaphysics of counterfactuals.
Opportunities galore for symbiosis between Big Data and causal inference.

Bruno Teixeira
Aug 30, 2020 rated it liked it
I had countless discussions with my thesis supervisor on the causes of road traffic accidents. I was proposing to build an AI model to predict which variables had more impact and all I had was an enormous dataset. For a student, starting to create an hypothesis to explain the influence of the causal variables in the outcomes seemed like running in circles. A few months later I found what could have helped me to run in a straight line - The Book of Why - and its promise to establish the causal re ...more
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Judea Pearl is an Israeli-American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks.

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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.” 11 likes
“You cannot answer a question that you cannot ask, and you cannot ask a question that you have no words for.” 10 likes
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