- Paperback: 432 pages
- Publisher: Penguin (2019)
- Language: English
- ISBN-10: 0141982411
- ISBN-13: 978-0141982410
- Product Dimensions: 12.9 x 2.4 x 19.8 cm
- Customer Reviews: 292 customer ratings
- Amazon Bestsellers Rank: #7,757 in Books (See Top 100 in Books)
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The Book of Why: The New Science of Cause and Effect Paperback – 1 January 2019
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If causation is not correlation, then what is it? Thanks to Judea Pearl's epoch-making research, we now have a precise answer to this question. If you want to understand how the world works, this engrossing and delightful book is the place to start -- Pedro Domingos, professor of computer science, University of Washington, author of The Master Algorithm
Judea Pearl has been the heart and soul of a revolution in artificial intelligence and in computer science more broadly -- Eric Horvitz, Technical Fellow and Director, Microsoft Research Labs
Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence ... and they have redefined the term 'thinking machine' -- Vint Cerf, Chief Internet Evangelist, Google, Inc.
Modern applications of AI, such as robotics, self-driving cars, speech recognition, and machine translation deal with uncertainty. Pearl has been instrumental in supplying the rationale and much valuable technology that allow these applications to flourish -- Alfred Spector, Vice President of Research, Google, Inc.
About the Author
Judea Pearl is a world-renowned Israeli-American computer scientist and philosopher, known for his world-leading work in AI and the development of Bayesian networks, as well as his theory of causal and counterfactual inference. In 2011, he won the most prestigious award in computer science, the Alan Turing Award. He has also received the Rumelhart Prize (Cognitive Science Society), the Benjamin Franklin Medal (Franklin Institute) and the Lakatos Award (London School of Economics), and he is the founder and president of the Daniel Pearl Foundation.
Dana Mackenzie, a Ph.D. mathematician turned science writer, has written for such magazines as Science, New Scientist, and Discover.
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The Book of Why is STUFFED with countless cases & concepts, dealing with Cause & Effect - aka the Science of Casualty.
Warning: This IS A NERDY, formulae laden book. If you can ignore the math & wade beyond, you will learn some things! E.g.
- How does your cell phone manage to convert your call and relay it to the other person almost perfectly? (Bayesian Networks)
- Path diagrams
- Belief propagation
- Monty Hall Paradox (amazing)
- How to train AI
"One of the crowning achievements of the Causal Revolution has been to explain how to predict the effects of an intervention without actually enacting it"
"Ancient Greek philosopher Democritus said, "I would rather discover one cause than be the King of Persia."
- "Galton conjectured that regression to the mean was a physical progress...nature's way of ensuring and adjusting distribution..." (my fav)!
Take a shot at the book. It's complicated BUT worth it
Bhupendra Madhiwalla, Mumbai, India
Top international reviews
Pearl's book is similar. His views and methods on causality are important but they are not the only possible way forward even if he is convinced that they are. What he proposes is a new graphical way of looking at scientific problems that allows you to understand causality. His bête noire is statistics which he sees as having obstructed the development of causal theories for the last century. I have to declare here than in some ways I am a statistician and I find his constant going on about how bad statistics is while then using the same language and equations as statistics somewhat annoying. He is right that the founders like Fisher and Pearson were bullies thugs and dictators who straight-jacketed their science for many years. But the Bayesians have largely undone there mistakes. What statisticians are is pedantic, but so are philosophers. Popper tells us we can only disprove and never prove anything but I am pretty sure that the Earth goes around the sun. Pearl is squally pedantic in describing what he will and will not allow to be called causal inference and he creates his own do calculus to represent this. But this has to be reduced to conditional probability (statistics) in order to be able to use data to solve.
His diagrams are very useful but again I am unconvinced by the proofs of completeness offered and by the claims that it is a completely objective system. It depends on what terms researchers put in the diagram. Pearl is right that the statisticians were too pedantic and so excluded causal arguments but in trying to establish his method as completely objective I think he falls into the same trap. We have to accept that science is never completely objective. We are always restricted by our language, metaphor and the current state of our imagination. This is not to say his method is not a step forward. It is just to say he claims too much.
This book was written to make Pearl's views more accessible and it is written with a co-author whose presence only shows itself as an example in a later chapter. Most of the time it is written in the first person which is odd for a book with two authors. It is part biography, part history and part textbook. For the most part it succeeds in its aim but the chapters on counter-factuals and mediation are definitely not an easy read and need much better explanations. So while the ideas are important it just doesn't quite deliver them in an accessible way.
He organises his argument using the three-runged “ladder of causation”. On the bottom rung is pure statistics, reasoning about observations: what is the probability of recovery, found from observing these people who have taken a drug. The second rung allows reasoning about interventions: what is the probability of recovery, if I were to give these other people the drug. And the top rung includes reasoning about counterfactuals: what would have happened if that person had not received the drug?
Intervention (rung 2) is different from observation alone (rung 1) because the observations may be (almost certainly are) of a biassed group: observing only those who took the drug for whatever reason, maybe because they were already sick in a particular hospital, or because they were rich enough to afford it, or some other confounding variable. The intervention, however, is a different case: people are specifically given the drug. The purely statistical way of moving up to rung 2 is to run a randomised control trial (RCT), to remove the effect of confounding variables, and thereby to make the observed results the same as the results from intervention. The RCT is often known as the “gold standard” for experimental research for this reason.
But here’s the thing: what is a confounding variable, and what is not? In order to know what to control for, and what to ignore, the experimenter has to have some kind of implicit causal model in their head. It has to be implicit, because statisticians are not allowed to talk about causality! Yet it must exist to some degree, otherwise how do we even know which variables to measure, let alone control for? Pearl argues to make this causal model explicit, and use it in the experimental design. Then, with respect to this now explicit causal model, it is possible to reason about results more powerfully. (He does not address how to discover this model: that is a different part of the scientific process, of modelling the world. However, observations can be used to test the model to some degree: some models are simply too causally strong to support the observed situation.)
Pearl uses this framework to show how and why the RCT works. More importantly, he also shows that it is possible to reason about interventions sometimes from observations alone (hence data mining pure observations becomes more powerful), or sometimes with fewer controlled variables, without the need for a full RCT. This is extremely useful, since there are many cases where RCTs are unethical, impractical, or too expensive. RCTs are not the “gold standard” after all; they are basically a dumb sledgehammer approach. He also shows how to use the causal model to calculate which variables do need to be controlled for, and how controlling for certain variables is precisely the wrong thing to do.
Using such causal models also allows us to ascend to the third rung: reasoning about counterfactuals, where experiments are in principle impossible. This gives us power to reason about different worlds: What’s the probability that Fred would have died from lung cancer if he hadn’t smoked? What’s the probability that heat wave would have happened with less CO2 in the atmosphere?
[p51] "probabilities encode our beliefs about a static world, causality tells us whether and how probabilities change when the world changes, be it by intervention or by act of imagination."
This is a very nicely written book, with many real world examples. The historical detail included shows how and why statisticians neglected causality. It is not always an easy read – the concepts are quite intricate in places – but it is a crucially important read. We should never again bow down to “correlation is not causation”: we now know how to discover when it is.
The definition og causality is so important, because it determines the time direction of the future and the past. We can only remember the past, not the future. Any intelligence (artificial og natural) must involve causality. This book is about how a new science of cause and effect can be joined to statistics, so a robot with real humanlike intelligence can be created (eventually).
This implies that Google's DeepLearning and TensorFlow cannot possibly be real intelligence. They are data driven like classical statistics and do not allow causality.
Judea Pearl vermittelt auch dem Nicht-Statistiker auf anschauliche Weise, was es mit der dreistufigen "Leiter der Kausalität" auf sich hat, und warum erst Intervention und kontrafaktisches Schlussfolgern den Schlüssel zu einem Weltverständnis bieten, wie es bislang nur homo sapiens vorbehalten war. Heutiger Künstlicher Intelligenz fehlt jegliches Kausalverständnis, selbst wenn sie wie AlphaGo oder Watson in Teilbereichen auf übermenschlichem Niveau agiert. Doch ausgerüstet mit dem von Pearl dargestellten Handwerkszeug wird sich von nun an eine ganze Generation von Ingenieuren und Computerwissenschaftlern daran machen, die Künstliche Intelligenz in voller Breite auf das Niveau des Menschen zu hieven - und darüber hinaus. Sollte dabei etwas gehörig schiefgehen, wird sich vielleicht die Menschheit rückblickend fragen, ob dieses Buch besser nie erschienen wäre. Was dann übrigens wieder eine kontrafaktische Betrachtung wäre, mithin ganz im Sinne von Pearl.
The idea of drawing causual relationships, based on prior knowledge and hypothesis, before diving into correlation analysis, makes a lot of sense. The causual diagrams are very useful, as are the mediators and confounders concepts.
There are ample examples out there of people using data (observations, not based on experiments) to conclude on causality, where those conclusions cannot be made.
Most recently I have seen many examples of boiling multi-factor problems like age- and gender-"inequality" into one-factor problems and labeling them as "discrimination explains it all". Often with basis in correlation studies, with no deeper (or even superficial) understanding of confounding factors. Many people would benefit from learning the basics of this book. But, like people who needs ethics courses are not likely to take them, those who need a deeper understanding of causality, are not likely to be interested in reading about it.
A great read - difficult at times - but definitely worth the time.
I will make my teenage kids read sections of it, just to tease their curiosity and open their eyes. The diagrams are a great tool for that.
Das Buch arbeitet zunächst den Unterschied zwischen einer Korrelation (gemeinsames Auftreten von Ereignissen A und B) und einem Kausalzusammenhang (Ereignis A führt zu Ereignis B) heraus. Darauf aufbauend wird ein Formalismus zur Beschreibung von Kausalzusammenhängen (causal trees, „do“-calculus) entwickelt. Diese Methoden werden dann zur Analyse einer Reihe von statistischen Problemen eingesetzt. Dabei weisen die Autoren ausführlich auf mögliche Fehlinterpretationen der Ergebnisse der üblichen statistischen Methoden hin. Besonders lehrreich ist in meinen Augen auch die Analyse von unterschiedlichen statistischen Paradoxa wie z.B. das bekannte Ziegenproblem (Monty-Hall-Problem). Überlegungen zur Verwendung der Methoden im Zusammenhang mit Künstlicher Intelligenz schließen das Buch ab.
Das Buch ist eine lesbare Form der Forschungsarbeiten von Prof. Pearl. Alle Konzepte werden mit reichhaltigen Beispielen und Einführungen versehen. Trotzdem ist der Inhalt nicht ohne Anspruch. Es werden im Laufe des Buchs auch einige mathematische Formeln entwickelt. Die Leser, die das nicht abschreckt, werden mit einigen Einsichten über Statistik belohnt. Zumindest ging es mir so, obwohl ich mich schon seit über 20 Jahren beruflich mit statistischer Analyse beschäftige. Beispielsweise hat mich die Einfachheit und Eleganz beeindruckt, mit der sich das Ziegenproblem durch die Werkzeuge in dem Buch analysieren lässt.
Pearl ist ein leidenschaftlicher Verfechter seines Ansatzes. Seine Kritik an den bisherigen Methoden fällt an einigen Stellen sehr harsch aus. Auch sind manche Einführungen für meinen Geschmack etwas zu ausladend ausgefallen. Das Sendungsbewusstsein des Autors tritt an diesen Stellen deutlich hervor. Trotzdem kann ich jedem, der sich mit Statistik beschäftigt, dieses Buch wärmstens ans Herz legen.
Coming to the point of AI, it doesn't as yet exist, and cannot be created by human programming. Machines must be able to think for themselves, and no amount of programs to yeach them how a result is generated by an action will do that. We don't even know ourselves whether we know the cause, we only think we do. I don't see true AI ever being a possibility. Transposing our limited knowledge onto a piece of silicon which can only calculate in 0s and 1s is a fool's errand. Trying to turn basee elements into gold.
Lots of rehashing to do, but it is quite rare when a single text shows you the possibilities for a morally- sound computational future!
Highly recommended - am curious on potential applications, e.g. implementation of counterfactual algorithms