Causality: Models, Reasoning, and Inference by Judea Pearl (PDF)

Description

Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable. Professor of Computer Science at the UCLA, Judea Pearl is the winner of the 2008 Benjamin Franklin Award in Computers and Cognitive Science.

User’s Reviews

Editorial Reviews: Review “…thought provoking and [a] valuable addition to the scientific community. The author, Judea Pearl, is not only an expert but also well known for creating novel ideas in cognitive system analysis and artificial intelligence…It is a well-composed an written book. The bibliography is exhaustive and up-to-date. I enjoyed thoroughly reading the material in the book. I would highly recommend this book to both theoretical and applied scientists.” Journal of statistical Computation and Simulation”Without assuming much beyond elementary probability theory, Judea Pearl’s book provides an attractive tour of recent work, in which he has played a central role, on causal models and causal reasoning. Due to his efforts, and that of a few others, a Renaissance in thinking and using causal concepts is taking place.” Patrick Suppes, Center for the Study of Language and Information, Stanford University”For philosophers of science with a serious interest in casual modeling, Causality is simply mandatory reading.” Philosophical Review”This highly original book will change the way social science researchers think about causality for years to come. Pearl has produced a new and powerful formal theory of causal analysis that will be great use to the serious empirical researcher. A must read.” Christopher Winship, Department of Sociology, Harvard University”Judea Pearl’s previous book, Probabilistic Reasoning in Intelligent Systems, was arguably the most influential book in Artificial Intelligence in the past decade, setting the stage for much of the current activity in probabilistic reasoning. In this book, Pearl turns his attention to causality, boldly arguing for the primacy of a notion long ignored in statistics and misunderstood and mistrusted in other disciplines, from physics to economics. He demystifies the notion, clarifies the basic concepts in terms of graphical models, and explains the source of many misunderstandings. This book should prove invaluable to researchers in artificial intelligence, statistics, economics, epidemiology, and philosophy, and, indeed, all those interested in the fundamental notion of causality. It may well prove to be one of the most influential books of the next decade.” Joseph Halpern, Computer Science Department, Cornell University”Judea Pearl has come to statistics and causation with enthusiasm and creativity. His work is always thought provoking and worth careful study. This book proves to be no exception. Time and again I found myself disagreeing both with his assumptions and with his conclusions, but I was also fascinated by new insights into problems I thought I already understood well. This book illustrates the rich contributions Pearl has made to statistical literature and to our collective understanding of models for causal reasoning.” Stephen Fienberg, Maurice Falk University Professor of Statistics and Social Science, Carnegie Mellon University”This book on causal inference by a brilliant computer scientist will both delight and inform all–philosophers, psychologists, epidemiologists, computer scientists, lawyers–who appreciate the intriguing problem of causation posed by David Hume more than two and a half centuries ago.” Patricia Cheng, Department of Pyschology, University of California, Los Angeles”This book fulfills a long-standing need for a rigorous yet accessible treatise on the mathematics of causal inference. Judea Pearl has done a masterful job of describing the most important approaches and displaying their underlying logical unity. The book deserves to be read by all statisticians and scientists who use nonexperimental data to study causation, and would serve well as a graduate or advanced undergraduate course text.” Sander Greenland, UCLA School of Public Health”Judea Pearl has written an account of recent advances in the modeling of probability and cause, substantial parts of which are due to him and his co-workers. This is essential reading for anyone interested in causality.” Brian Skyrms, Department of Philosophy, University of California, Irvine”In conclusion, make no mistake about it: This is an important book. Even if almost all of the content has appeared previously in diverse venues, it has been brought together here for all of us to think about.” Journal of American Statistical Association, Charles R. Hadlock, Bentley College Book Description Causality offers the first comprehensive coverage of causal analysis in many sciences.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This is a very interesing book that Judea Pearl worte. The topic is currently of general interest for diverse fields as economics, social sciences and biology, however, this book is not intended for practitoners from these field who face a special problem and search for a possible solution. If you want to buy this book for this reason you will not be able to extract this information for this book. The reason therefor is that important technics like Bayesian Networks or Structural Equations are treated in 3 pages in each case. Judea Pearl assumes that the reader is already familiar with such methods beforehand. (Readers interested in the later subject are strongly refered to Bollen’s book “Structural Equations with latent variables”.)Moreover, I do not think that this book presents state of the art information about our current knowledge of this subject. For example, the important problem to extract a network structure (structure learning) from data rather than estimating the parameters of a given networks structure is completely missing.Nevertheless, this is a good book, because it might give you in the long run (you can not read it in one piece) insights you did not have before. Of course not to all topics causality is involved (see, e.g., above) but the given topics are thorough explained albeit on an advanced level.Update: I add one star (total three) to my evaluation, because in the meanwhile I appreciate the historical development described in the book including references to the literature.

⭐I needed this for my research and found the price and quality. Unfortunately, the content was tedious reading and failed to illuminate the subject as well as other tomes I’ve read.

⭐great

⭐Great deal, thanks. Reading this book as part of a special session with one of my professors. Looks interesting and challenging.

⭐Judea Pearl is one of the leading researchers in the topic of causality. What is causality? In the exploration of statistical data we are often able to find relationships or correlations between two variables. We are often tempted to attribute the results of one variable, say A as an outcome (being high or low)that is due to the result (high or low) of the other, say B. We want to say that B is the cause of the outcome of A. Significant correlation by itself only suggests relationships. It cannot tell you whether A causes B or B causes A or neither. Causality is the study of designing experiments to allow you to determine if a relationship has a cause and effect. The subject matter is very philosophical and somewhat controversial. But a lot of research effort has gone into providing mathematical rigor to the concept. Pearl is one of those rare scientists who can contribute to such theory and explain it. But as Aickin suggests in his amazon review this is not a subject for a novice. Previous exposure to statistical methods such as correlation and regression is important to a clear understanding of this book.

⭐The scientific research community has adopted rigorous methods to eliminate the need for subjective judgments about many things, but when it comes to testing whether X causes Y, they revert to intuition and hand-waving. This book makes a strong argument that we shouldn’t accept that. It demonstrates that it is possible to turn intuitions about causation into hypotheses that are unambiguous and testable.But the style is sufficiently dense and dry we will need some additional books with more practical styles before these ideas become widely understood. The style is fairly good by the standards of books whose main goal is rigorous proof, but it’s still hard work to learn a large number of new concepts that are mostly referred to by terse symbols whose meaning can’t be found via a glossary or index. Pearl occasionally introduces a memorable word, such as do(x), the way a software engineer who wants readable code would, but mostly sticks to single-character symbols that seem unreasonably hard (at least for us programmers who are used to descriptive names) to remember.If you’re uncertain whether reading this book is worth the effort, I strongly recommend reading the afterword first. It ought to have been used as the introduction, and without it many readers will be left wondering why they should believe they will be rewarded for slogging through so much dry material.

⭐Here, Judea does a great job presenting causal reasoning in a comprehensive and intuitive manner by reimaging otherwise complicated relations with directed acyclical graphs to visually illustrate the relations between variables.

⭐Diseño de alternativas

⭐

⭐I was interested in this book from a business point of view, using the concepts in a financial risk environment. It’s well written but a bit academic for what I wanted.

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