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

I hate to tell you this, but science, at least real science, has linked cause and effect. That's basically how science works. What this book is doing is trying to explain why the social "sciences" have yet to link cause and effect, and the simple answer is that the social "sciences" is not real science. The social world works by interweaving individual factors into social dynamics that create new emergent products. In science, you isolate variables to determine cause and effect. You can't do this with social systems. It's like trying to make sense of a sentence by separating each word and then each letter. The sentence only makes sense as a whole. This author is just trying to add more math and sciency formulas to the social sciences to make it look more scientific. I studied Economics. This is exactly what they do in Economics and have yet to prove anything or link any cause to effect, simply because economic systems are far too complex and can't be separated and isolated like a lab experiment. What the entire farce is doing is called obfuscation. If you are so confused by the math, technical jargon, sciency graphs and tables and data and figures, then you just feel dumb and agree with whatever idiotic conclusion the author invents. Look how cutting taxes and increasing federal spending stimulates the economy with all my sciency charts and formulas! It's an entire scam industry and this author is just like another grifter.

The Book of Why is a popular introduction to Judea Pearl’s branch of causal inference. But it is also so much more.Pearl has written many other textbooks introducing his graphical approach. But in this book, Pearl provides an engaging narrative of the history of causal inference, the important distinctions he sees in his branch and its importance for the future of Artificial Intelligence.Briefly, Pearl views classical statistics as seriously flawed in not having developed a meaningful theory of causality. While able to demonstrate correlation, Pearl asserts that in classical statistics all relationships are two-way: that is 2x=3y+6 can also be written 3y=2x-6. We are left in doubt as to whether x causes y or y causes x.Fundamentally, Pearl sees this problem as still plaguing all artificial intelligence and statistics. In its place, Pearl argues that the exact causal relationship between all variables should be explicitly symbolized in graphical form and only then can mathematical operations tease out the precise causal effect.To be transparent, I am trained in the Rubin approach to causal inference and disagree with some of Pearl’s history and characterization of statistics. But that is not the point. The history is well-written, engaging and understandable by the lay reader. Similarly, his account of graphical causal inference theory is followable even for someone like myself who did not learn these techniques in graduate school.The last part of the book, where Pearl opines on the future of AI, is the most sensational. Pearl believes that if computers were programmed to understand his symbolization of causal inference theory they would be empowered to realize counterfactuals and thus engage in moral decision making. Furthermore, since Pearl himself was a pioneer in deep learning, his characterization of contemporary AI as hopelessly doomed in the quest to replicate human cognition because of a lack of understanding in causal inference will be sure to garner attention.But one would be misguided to think that speculations about AI or mischaracterizations of other kinds of causal inference make this book any less of a classic. For the first time, Pearl has written a popular, interesting and provocative book describing his branch of causal inference theory—past, present and future.This book is a must read then, not only for causal inference theorists, but more widely for those with any interest in contemporary developments in computer science, statistics or Artificial Intelligence. A book that, like Kahneman’s Thinking Fast and Slow, is a triumphant summary of a lifetime of work in scientific topics that have ramifications, not only for fellow scientists, but for all of humanity.

Wow! I am a physician epidemiologist with a doctorate in epidemiology and I teach computational epidemiology (with R) at UC Berkeley. I had the opportunity to study biostatistics from the best professors at UC Berkeley School of Public Health (Steve Selvin, Nicolas Jewell, Richard Brand, and many more). The field of causal inference was just beginning to take off with biostatisticians piloting the plane (Mark van der Laan, Nicolas Jewell, etc.). I avoided a rigorous study of causal inference but eventually came around after studying Bayesian networks for decision analysis (FYI: Pearl pioneered Bayesian networks). Judea Pearl's Bayesian networks and causal graphs connects the fields of statistics, epidemiology, decision and computer sciences in a profoundly elegant way. His work empowers and expands the potential of "big data." This is the first book written for the general public on this topic. It will have a **huge impact**. Causality and causal reasoning is at the core of everything we see, do, and imagine. He provides a graphical tool (causal graphs) for encoding expert knowledge (including community wisdom and experience). Anyone --- yes, anyone --- can learn the basics. For additional rigor, there are structural causal models (functional equations). I now consider it data science "malpractice" to design studies, analyze data, or adjust for confounders without using causal graphs. As he covers extensively in the history of causality, human brains are wired to resist new paradigms. Be intellectually wise and humble and read this book -- you will not regret it!

This book represents the collaboration of Judea Pearl, a professional expert on causality, and Dana MacKenzie, an excellent science writer who is also a mathematician. It addresses the complicated differences between attempting to truly establish that A causes B versus erroneously asserting that A and B must be cause and effect because they correlate. The book is not a quick read. But the subject is of sufficient importance to merit the effort to digest its complex analyses. The section in the book treating the subject of climate change illustrates some of the challenges facing those seeking to truly understand what we know and don't know about this issue. Other examples are drawn from the field of medical research and other disciplines. A useful book written with great intelligence.

I am always cautious when a book proclaims to be about a new science. I am reminded of Albert-Lazlo Barabasi's Linked and Stephen Wolfram's a New Kind of Science. They make big promises but they often fail to deliver, or what they are delivering is something which is more a rediscovery rather than something new.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.

We have all heard the old saying “correlation is not causation”. This is a problem for statistics, since all it can measure is correlation. Pearl here argues that this is because statisticians are restricting themselves too much, and that it is possible to do more. There is no magic; to get this more, you have to add something into the system, but that something is very reasonable: a causal model.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.

This is not a book on cause and effect in physics. Instead it tells the story og how classical statistics was separated from cause and effect by its development as a mathematical transformation (a so called "reduction") of observed data, independent of how and why these data were measured. It was argued the the statistical results should be objective without any intervention in the observational process. The resulting correlations cannot, however, tell us anything about cause and effect. R. A. Fisher invented (in 1924) the randomized controlled trial in order to avoid a subjective intervention. This is the old science of cause and effect.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.

Assume you are a lawyer. Your client fired a gun at someone. The bullet missed & the person ran away in fear. While running, a piano fell from a window above & killed that person. The Police want to charge your client for murder. You propose a counter argument using cause & effect.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- RCTs- Monty Hall Paradox (amazing)- How to train AINice quotes:"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

First 2 chapters ok but found it hard going then and have temporarily given up. Did A level maths and a science degree but still found the logic quite hard. Think that is probably necessary given the subject. relates to the difference between the reasoning and understanding of computers and of people. And why the current gap is still there and difficult to cross. (Maybe thankfully!)