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

Judea Pearl,

Dana Mackenzie (Goodreads Author)

3.95 · Rating details · 3,180 ratings · 402 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 ...more

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Hardcover, 432 pages

Published May 15th 2018 by Basic Books (first published 2018)

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Sep 03, 2018 nostalgebraist 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

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Dec 05, 2018 Andrew Harlan rated it liked it · review of another edition

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 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." He shows that once you know the causal relations (or lack thereof) between relevant variables, you can use graphical methods to work out how to estimate the values of the parameters you are interested in. But this puts the cart before the horse.

In The Book of Why and many earlier publications, Pearl promotes his extension of probability calculus and nonparametric structural equation models (directed acyclic graphs or DAGs) as the solution to the problem of inferring causes from observational data. He describes his approach with such terms as "revolutionary" and "miraculous." I have personally found traditional path or SEM models useful when thinking about some causal problem, but I see little new that is worthwhile in Pearl's approach. Graphical models can be useful tools in research, but there's nothing revolutionary about them; they are not a royal road to causality. If you have good subject knowledge about a research topic, you will have an understanding of the dependencies between relevant variables and you can use graphical methods as one of the tools to clarify the implications of the model, but that's really it.

What is missing from Pearl's book is very telling. Almost all the "practical" examples in the book concern either problems that were long ago solved with methods other than Pearl's (e.g. smoking causing lung cancer) or else are toy problems where the underlying causal model is presumed known. Pearl triumphantly shows how his approach "solves" these non-problems. He delights in how he can routinize the procedures for solving various inference problems when the causal structure is known with certainty. I must say that I find his explanations of well-known paradoxes less illuminating than more traditional treatments. For example, tabular data on the distributions of values for different groups usually makes Simpson's paradox entirely transparent, obviating the need for a more formal treatment. More importantly, Pearl's methods do nothing to help discover whether your data are actually confounded by something like Simpson's paradox.

A prominent early airing of Pearl's ideas about causality took place in the journal Biometrika in 1995. Pearl's target paper published in the journal contained all the main elements of his current framework. The paper was accompanied by a number of expert commentaries, and a common theme in many of them was, aside from polite comments about the technical elegance of the approach, a skepticism that a method like this could help achieve concrete, real-life advances in scientific understanding. The 1995 article and the 2018 book are separated by 23 years, and in the meantime Pearl has published two editions of his textbook on causal inference, but there is still a total paucity of real-world benefits stemming from Pearl's program. The implications of this state of affairs for Pearl's "causal revolution" are devastating, but he seems to be blind to the enormous gap between his pompous pronouncements and the reality.

If Pearl's claims about the revolutionary impact of his theory were correct, we would now be living in a golden age of science. In reality, however, there is currently a crisis of confidence in science, across many fields. Not only has the increasing utilization of Pearl's approach not done anything to prevent the crisis. It's also that Pearl completely failed to anticipate the actual problems that many fields of research are facing. No amount of DAGs will solve such problems as small sample sizes, selective reporting of analyses, lack of replication, weak theories and poor measurement.

Pearl assumes that researchers can make use of a reliable body of "background knowledge" when drawing their causal diagrams. If the relevant variables and their causal relations are presumed known, the estimation of causal parameters becomes straightforward. This shows an undue optimism on his part. The fact is that, as has become ever more obvious in recent years, many fields of research not only lack convincing theories to make sense of data and experiments, but there is also pervasive uncertainty about the basic reliability of large swaths of observational and experimental data reported in the published literature. All scientific knowledge ultimately relies on at least some uncertain assumptions, but that does not mean that you can freely make any old assumption when making causal inferences. If the "background knowledge" you have is just a muddle of unconnected, incompatible and unreplicated findings and hypotheses, Pearl's method is not useful.

You might protest here that even if Pearl's methods are impractical in immature fields, that's not the case when the background knowledge available is more solid. However, Pearl does not even try to argue that his methods are needed in the mature, physical sciences. Researchers in mature fields already know how to make causal inferences and have no need for Pearl's insights. If you have a strong theory and good data, it is usually the case that a simple regression or the like will give you the causal estimates you want. In such cases, nothing is gained by re-expressing a problem and its solution in terms of Pearl's formalisms. If you have a strong theory, Pearl's methods offer nothing to you, whereas if your theory is weak, as is by and large the case in social science, DAGs will only give a false causal veneer to associations whose nature is unclear.

Therefore, Pearl's "revolution" amounts to introducing canned procedures for dealing with some relatively trivial subproblems that a research program may contain and that a mature discipline knows how to deal with anyway. It does nothing to help tackle the hard problems of research, so it is unsurprising that the (sporadic) adoption of these methods has not coincided with any scientific progress.

Pearl's program is a failed attempt to replace the messy, trial-and-error process of scientific discovery with clean mathematical and logical formalisms. It is an overreach of rationalism by a mathematically inclined theoretician with little experience dealing with the jumbled reality of the "soft" sciences.

With all that said, I nevertheless think The Book of Why is a decent read. In particular, the historical chapters are interesting (even if sometimes tendentious) and a non-expert reader will get the gist of the method from the many examples (even if some of the exposition is overly technical). Just don't expect to find anything revolutionary in it. ...more

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May 24, 2018 Terran M 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 ...more

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May 21, 2018 Alex Telfar 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 ...more

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Jan 13, 2019 Gary Beauregard Bottomley rated it liked it · review of another edition

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 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. ...more

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Jan 02, 2019 Ryan Sloan rated it it was ok

Shelves: cognitive-science, modeling, technology

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 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 on this, and your mileage may vary.

Popular science writing is hard. Balancing the seeming impenetrability of a technical subject with the sin of oversimplification is never straightforward. This is why I have a huge amount of respect for great science writers, and why I feel that they often do a better job writing these books than the experts themselves. When I saw that a renowned scientist team up with a science writer on this book, I was very excited. Unfortunately, I feel this book is meandering, poorly justified, and shallow in some important areas. It's in dire need of a more aggressive editor, and a more reader-focused (or outspoken) science writer. The tone throughout suggests this was primarily Pearl's work, with Mackenzie along for the ride.

Rather than organizing this book around big ideas, they meander through the history of statistics with a tone verging on vindictive. The reader is likely to come away with a not-so-rosy impression of Pearl as a self-aggrandizing person with an axe to grind. Pearl devotes countless pages to rambling diatribes about statisticians with whom he disagrees, and continuously suggests that had they only used his championed theory of causality, they wouldn't have been so dumb. It's kind of unseemly how much energy he pours into these rants about how they could've gotten it right. I get it. But I think this can be done in a way that is tasteful and honors the contributions of those who came before (in fact, I just read Thaler's Misbehaving which is a great example of doing this well.)

Which brings me to my next point, which is that, as far as I can recall, nearly every application of causal diagrams that Pearl mentions is in the form of post-hoc rationalization. I was shocked at how few instances there were of these causal diagrams actually being used to solve problems, instead the book was dominated by "if only"s. If only they'd used them when analyzing whether cigarettes caused cancer, if only they'd used them when analyzing the relationship between birth weight and birth defects, if only if only if only.

It leaves me in a very contradictory state of mind: on the one hand, I must design the causal diagram a priori before using the data to draw conclusions; on the other hand, Pearl seems to greatly reap the rewards of hindsight.

Most of the problems that Pearl works through are toy problems, designed to illustrate principles. There is nothing wrong with that (I am a huge believer in worked examples), but he rarely works through them in depth! I am reminded of so many (bad) math textbooks which overused the "proof is left as an exercise for the reader" trope. For example, in Chapter 7 (page 239-240) Pearl writes the following:

Yet another reason that the do-calculus remains important is transparency. As I wrote this chapter, Bareinboim (now a professor at Purdue) sent me a new puzzle: a diagram with just four observed variables, X, Y, Z, and W, and two unobservable variables U1 and U2. He challenged me to figure out if the effect of X on Y was estimable. There was no way to block the backdoor paths, and no front-door condition. I tried all my favorite shortcuts and my otherwise trustworthy intuitive arguments, both pro and con, and I couldn't see how to do it. I could not find a way out of the maze. But as soon as Bareinboim whispered to me, "try the do-calculus," the answer came shining through like a baby's smile. Every step was clear and meaningful. This is now the simplest model known to us in which the causal effect needs to be estimated by a method that goes beyond the front- and back-door adjustments.

Reading this glowing paragraph, I couldn't wait to see such an elegant, "baby's smile" of a proof. Are you ready for it? Well I don't know it because they never shared it. Pearl just moves on. All that hype and nada. I actually wrote "are you kidding me?" in the margin of my book. It's full of stuff like this - flowery prose with a lack of concrete examples. I did find that there was a good worked example for counterfactuals beginning on page 273 (the "what would Alice's salary be if she had a college degree?" example). The lack of examples would be fine if concepts were well-explained, but to be honest I was unimpressed with his explanation of even simpler statistical concepts. Heck, I think the reader could get through all 370 pages without actually learning what the back-door criterion is (he sort of defines back-door adjustment on page 220, if you're looking.) ...more

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Jun 11, 2018 Nilesh 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 ...more

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Jan 19, 2019 Andy 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, 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, saying that the way epidemiologists figured out that smoking causes lung cancer is somehow pathetic compared with his approach. The Hill approach saved millions of lives. What was wrong with it? What is the equivalent accomplishment of his little arrows? He says Hill wasn’t quantitative, but that’s not really true because the Hill Criteria include magnitude of effect, etc. And of course, the whole smoking/cancer thing is long before Pearl’s work but is an example of what he labels his “revolution” , i.e. that observational data can give us evidence of causality. I have a feeling the emperor is naked here, but I will check on it more before lowering the score, and I won’t need a diagram to figure it out.

--Update February 2019:
I went over the smoking chapter in detail. The emperor is naked.

The idea that one can establish causality without randomized trials is extremely important, but as Pearl himself points out, it predates the causal diagrams. This book does not make a clear case for the causal diagrams being the amazing conceptual revolution that the author claims they are. Perhaps he should diagram that causal pathway.

Pearl leaves out some important details of the tobacco story (like the Readers' Digest article that alerted the American public to the risk of cigarettes) but even just going by what Pearl includes in his version, there was actually a very rapid penetration of the idea that cigarettes caused the lung cancer pandemic. Doll and Hill STARTED their study in 1948. In 1953, EVEN THE TOBACCO COMPANY SCIENTISTS accepted as fact that cigarettes cause lung cancer.

The problem with wider dissemination was that there was a massive campaign of disinformation by the tobacco industry and their minions. The Hill Criteria helped to seal the deal at a broader level despite the propaganda. There is no evidence offered that things would have moved along faster if people had been using causal diagrams. It is very easy in retrospect to say now that of course we could have convinced people about the dangers of smoking if only ... except that as Pearl illustrates, some people were never convinced no matter what.

If causal diagrams have stopped a pandemic and saved millions of lives, then tell us that story.

For a better and beautiful book on epidemiology for a general audience, I would recommend:

Investigating Disease Patterns The Science of Epidemiology by Paul D. Stolley

...more

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Jun 06, 2018 Thiago Marzagão 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 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 the machine learning community. Much of what he says will not sound particularly Earth-shattering to people in (or from) the social sciences. "You can't learn causality from data alone, you need a model!" is one of the book's core messages. It's hard to see an economist or political scientist disagreeing with it. You come up with a theory, you think up its observable implications, you test them. Even Pearl's proposal that we use mediation analysis won't sound exactly novel. Social scientists have been doing that, they just don't use that name for it (they call it "testing the theory's microfoundations"). Now, having abandoned political science and lived among the machine learning people for four years now, I can see how Pearl's message is important to his intended audience. And social scientists should read the book too because it intelligently discusses the limitations of tools like RCTs and matching.

In the end what Pearl proposes - that we use our knowledge of how the world works in order to formulate and test hypotheses - may turn out to be (deservedly) influential in the machine learning community, but it won't help fix the core problem with the social sciences, i.e., that social scientists can always twist their hypotheses - not to mention the very questions they ask - to accomodate their pet world views. And when the Democrat/Republican ratio is 6:1, as it is in political science, we can't trust that people will keep each other honest - they won't. Pearl discusses in passing the possibility that some day we may have machine learning algorithms capable of producing their own causal models. Maybe then the social sciences will be worth the money they cost taxpayers. ...more

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Oct 08, 2018 Athan Tolis rated it it was amazing

Shelves: math-science, favorites

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 ...more

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Nov 18, 2018 Emre Sevinç 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 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 seemed to be relegated to a niche philosophical subfield.

Still later, when I started to deal with technological topics such as big data, as well as data science, I heard strong voices saying that what mattered was predictive power, and what did "really understanding something" even mean? Well, I thought, understanding something should mean constructing meaningful logical sentences that included at leas one well-grounded "because". Of course, being able to really understand things didn't matter much if the problem at hand was about guessing what a particular user would buy next, or watch next. It also didn't matter much if a black box could very accurately transcribe speech. But for many fields of data analysis, such as medicine, public policy, education, etc. I still find it very important to really understand things, that is, being able to talk about Ys causing Xs.

Finally, I have a beautiful exposition by one of the luminaries and revolutionaries of the causal revolution. The book doesn't help much in "discovering" causal relationships, but its focus on what it means to have a mathematical and solid graphical, algorithmic language to talk and communicate causation points to an achievement that is nothing less than a stellar. It also helped me to understand what Pearl has achieved after Bayesian networks, showing in many examples what it means to have "directionality", which was lacking in previous representations.

If the discussions and criticisms among statisticians such as https://stats.stackexchange.com/q/376920/26404 and https://stats.stackexchange.com/a/299090/26404 are any indication, this book will continue to generate much more heated discussion in the near future, that's for sure.

I can recommend this book to anyone involved with any scientific field that uses statistics, as well as people dealing with big data, machine learning, deep learning, and data science: the insights to be gained are invaluable. In a sense, this book marks the end of an intellectual tour de force by one of the greatest minds of reasoning. In another sense, this book also marks the perfectly solid starting point for the next phase of our long journey in understanding nature, including ourselves. ...more

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Jul 18, 2018 Daniel Christensen rated it it was amazing

Shelves: analytics, social-science-humanities, pop-science, life, liberal-arts-2-0

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 ...more

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Dec 04, 2018 Kelly Jade 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. ...more

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Aug 29, 2020 Juan 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 ...more

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Feb 13, 2021 Jakub 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

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Aug 09, 2018 Siddhartha Banerjee 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.

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Oct 22, 2019 José María rated it it was amazing

I wish I had known this stuff when I was doing my PhD.

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Nov 05, 2018 Karel Baloun 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 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 what modern world correlation economists, policy analysts and social scientists have taught for several decades!

On pg 64-65, Pearl elegantly distinguishes two models of how talent and luck generate success. If luck applies independently to each degeneration, the model is mathematically stable, but it it accrues (with talent) over generations you get a wide persistent distribution of outcomes. For me, this profoundly simplified economic inequality, and shows how useful is a rigorous framework for thinking. Without accruing “luck” you get reversion to the mean, and with it you get dynastic wealth. Yet with either one, talent is passed down generations, aiding success under equal opportunity.

Appropriately, in the engaging historical chapter 2, Pearl often asks Why historical persons thought as they did, whenever he is able to answer himself. These Why’s show how important causation is to understanding anything of importance. It helps the storytelling that the eminent statisticians Karl Pearson & Fisher are (through arrogance and dominance) the scientific bad guys. And it is consistent to see Fisher return in his evil cantankerous role during the tobacco trials and as a professor of Eugenics.

Pages 104-7 provide the most simple and lucid explanation of estimating the likelihood of having a disease from a positive test result for it. So useful.

Fun seeing how fuzzy math and Bayesian networks, cutting edge research when I was in grad school, have evolved into the mainstream. I can almost imagine my alternative life, had I studied this and developed ways to use it. 30 years ago honestly, I couldn’t have imagined it would be as widespread as it is today. Partly because back then we had no big data.

The paradox chapter is fascinating, and especially meaningful because it anchors the earlier theoretical ideas into memory.

The closing chapters are difficult, in terms of figuring out how to apply this sparkling and sharp tools to your own life and work. Well, I suppose that should be a challenge. I wish more work and opinions from outside of Pearl’s immediate academic lineage were included, since I don’t feel we are given a clear view of whether there are any, and how his work fits into the overall future of science.

Final chapter on Ai is only a start, and leaves little on which to build. Author’s assumption that a moral Ai could resolve all control problems feel shallow to me, however attractive! His assertion, that free will is superior in performance to borg like behavior from simulations like generative adversarial networks, feels unproven. ...more

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Jan 24, 2020 Alex Lee rated it it was amazing

Shelves: economics, science, philosophy, impressive, 2020, mathematics, info-tech

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 ...more

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Feb 03, 2021 Bilge 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.

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Jan 12, 2019 Vicki 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

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Apr 04, 2020 Irma Ravkic 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 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 got some outcome Y, but what would have happened if I haven't done X'?). Pearl claims that causal questions cannot be answered from data alone, and that is a very interesting claim in the world of deep learning and big data. Most of the machine learning models we have today merely describe or transform the data - but they're not very good at interpreting the data in terms of why something happened because they don't incorporate causal mechanisms. That means that most of the time in machine learning we talk about patterns and associations, and the next step is to think about causation and counterfactuals. Pearl doesn't dismiss completely the notion of finding an association - it's maybe a start into examining more deeply the reason why those associations exist. One of the things we want of our intelligent agents is for them to reason in (long) chains of causality. For example (Pearl), if our AI robot starts vacuuming at night and wakes us up, we say: "You shouldn't have vacuumed now" (counterfactual). The vacuum robots should realize that it doesn't mean we never want it to vacuum clean our bedroom, but it should understand that vacuum cleaning late at night when people sleep might make someone angry, but it's fine to vacuum clean late at night when nobody's at home. We're not even close to this in current AI, but many people think we are.
Some concepts in the book are too technical to grasp in an easy read and hard to follow without maybe seeing the research papers and full work of Judea Pearl, but the book really tries to give intuitions and real-world examples behind these complex mechanisms of causality modeling and inference. That being said, the book contains a lot of stories from our history that examine the (mis)use of causality. I might go back and read one more time to settle it in. In a more perhaps exaggerated fashion, I see this book (and work of Pearl) as a 'holy book' of AI we should always refer back to from time to time, amongst others. ...more

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Jul 17, 2018 Peter McCluskey 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 ...more

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May 22, 2020 Julia 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

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Aug 16, 2018 Jason Furman rated it it was amazing

Shelves: social_science, nonfiction, mathematics

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 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 combines a history of science with a number of specific examples (e.g., do cigarettes cause cancer or does an algebra program in Chicago increase math knowledge) along with some of the mathematics of his method. But mostly Pearl's method centers around writing causal diagrams with arrows that allow you to identify blockers, cofounders, and the like. The arrows and terminology was not familiar to me from econometrics but many of the conclusions and techniques were (e.g., RCTs, multiple regression and instrumental variables).

In some cases Pearl claimed a greater profundity than I was able to follow, for example I could understand the Bayesian interpretation of his argument but he claimed there was a bigger one. In other cases he claimed that his diagrams opened up entirely new paths to solving causal questions and understanding the results of statistical analysis. In all of these cases I confess that I mapped them into my previous understanding rather than expanding, changing, evolving my previous understanding--and am unsure if this represents my limited understanding of his book or his overclaiming about his ideas, many of which were well understood and implemented in econometrics before.

The last chapter on AI, free will, explicability, and correlation vs. causation in big data was quite interesting but a bit of a departure from the rest of the book.

Overall, would recommend this to economists or others who are very interested in statistical analysis, it takes some effort at times (nothing like a textbook, which would be the best way to assess the novelty of some of the ideas), but amply rewards it. ...more

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Jun 23, 2018 Jayson Virissimo added it

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.

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Jan 08, 2020 Xinyu rated it it was amazing

Shelves: computer-science, statistics-machine-learning, 2008-2018

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 ...more

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Aug 30, 2020 Bruno Teixeira 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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Feb 26, 2020 Héctor Otero rated it it was amazing · review of another edition

An eye-opening book for any user of statistics. Being able to clearly explain and check our causal assumptions about the world is the key to be able to answer all sorts of queries about it. Not only those related to observations (e.g. how likely it is that it's rained given that the floor is wet?) but also those involving interventions (e.g. what will happen to the demand if I raise the price of this product?) and even counterfactuals (e.g. would that person have died of lung cancer had he not s ...more

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Nov 19, 2020 Rachel rated it liked it

Shelves: nonfiction, science, 2020

I wasn't a fan of how this book was organized and written. I think its purpose could have been better served with a couple of lectures or a short textbook about the main ideas (do-calculus as applied to intervention queries & counterfactuals), rather than embedding it in excessive storytelling and vague philosophical thoughts.

For a technical reader, his "Causal Inference in Statistics: A Primer" book may be more of what you're looking for. Skimming the table of contents now (I haven't read the b ...more

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About Judea Pearl

Judea Pearl

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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.

Books by Judea Pearl

Causality: Models, Reasoning, and Inference

I am Jewish: Personal Reflections Inspired by the Last Words of Daniel Pearl

Causal Inference in Statistics: A Primer

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Heuristics: Intelligent Search Strategies for Computer Problem Solving (The Addison-Wesley series in artificial intelligence)

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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.” — 9 likes

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