Causality: Models, Reasoning and Inference 2nd Edition

by Judea Pearl (Author)

24 customer reviews

ISBN-13: 978-0521895606

ISBN-10: 052189560X

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Written by one of the preeminent 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, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 5,000 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interests to students and professionals 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.

Causality: Models, Reasoning and Inference

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Editorial Reviews

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"Make no mistake about it: This is an important book.... The field has no shortage of lively controversy and divergent opinion, but be that as it may, this is certainly one of the contributions that will bring this material further out of the closet and into the face of the broader statistical community, a move that we should welcome both as consumers and as testers of its utility."
Journal of the American Statistical Association

"Pearl's career has been motivated by problems of artificial intelligence, but the implications of this book are much broader. The distinctions he raises and the mathematical foundation he assembles are critical for every field of scientific endeavor. This updated edition of a modern classic deserves a broad and attentive audience."
H. Van Dyke Parunak, Computing Reviews

"Pearl's book is about probabilistic approaches to causality and it gives, especially, empirical researchers working with observational data an immense aid to their research. It also gives theoretical statisticians something to think about as it raises many issues of estimation for example in respective data generating processes. ... This work of Pearl's is an invaluable contribution to the current discussion on the topic of causal modeling. As described by the author his main objective of the book is to develop a framework that integrates substantive knowledge including counterfactuals (through new notations and concepts) with statistical data so as to refine the former and to interpret the latter."
Priyantha Wijayatunga, Significance

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Product details

Hardcover: 484 pages
Publisher: Cambridge University Press; 2nd edition (September 14, 2009)
Language: English
ISBN-10: 052189560X
ISBN-13: 978-0521895606
Product Dimensions: 8.5 x 1.2 x 10 inches
Shipping Weight: 2.2 pounds (View shipping rates and policies)
Average Customer Review: 24 customer reviews
Amazon Best Sellers Rank: #155,787 in Books (See Top 100 in Books)
- #154 in Books > Science & Math > Experiments, Instruments & Measurement > Methodology & Statistics
- #587 in Books > Textbooks > Science & Mathematics > Mathematics > Statistics
- #819 in Books > Science & Math > Mathematics > Applied > Probability & Statistics

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Great material, but would be written clearer

ByBobby Cholloon October 28, 2016

Format: HardcoverVerified Purchase

Great material, but would be written clearer. The writing/describing style make it more complicated to understand. Adding more example would be a great help. Overall excellent read.

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Grateful this scratches my brain-itch

Byturkeynoxon April 10, 2014

Format: Kindle EditionVerified Purchase

The idea of causality has always chewed at the back of my mind but I never really got the opportunity to study it up-close and personal. This book lets me do that. Judea Pearl knocks all the conventional bull**** to the side and says, essentially, in order to figure out causality we need a new and organized way of looking at things - and he demonstrates how to do that for most every profession and field of science that comes to mind. It would have been handy (as a layperson) to have a guide to some of the symbols used, but after consulting with a friend who was a math major in college, you can actually just google "mathematical symbols" and scroll down a list until you find the right one. This book can be read by a layperson with the caveat it wasn't meant for laypeople, so I can't knock it for not having a symbol guide. In fact, Pearl gives a guide in the beginning of the book that tells you which chapters will be meaningful to you if you aren't a mathematician, and then which chapters will delve into the math for each specific profession / field of science. This is well-organized and fun to read; it feeds my inner geek.

0Comment 7 people found this helpful. Was this review helpful to you?YesNo

Causality

ByJosé-Fernando PInedaon December 5, 2009

Format: HardcoverVerified Purchase

This is a very suggestive analysis on a quite forgotten by now subject: the study of causality in the social sciences. The author traces very much the original idea of Havelmmo on the nature of econometrics, and brings up to date in the study of several strands of social phenomena that have to do with the nature of causation in human behaviour. He makes use of the notions of bayesian statistics, probability theory, graph theory, correlation analysis and the otherwise called non recursive hierarchical models in social studies. Recommended to those persons who still believe one of the purposes of social studies is to identify and measure causal chains and mechanisms and not simply to focus on correlations and forecasting techniques without due regard to the notion of what causes what and how does it seem to operate in reality.

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Read this book; here's what you need to know

BySamuel W. Mitchellon January 30, 2011

Format: HardcoverVerified Purchase

If you are at all capable of understanding it, you must read this book. It gives a general, and theoretical, overview of a highly promising and quite technical theory of what causes are and how to use them in experiments and reasoning. This is applied to practical examples in a very wide range of fields. This is a major step forward in understanding causal reasoning specifically, and scientific reasoning generally.

If you haven't read the first edition:
First, read the Epilogue. Don't start at the beginning. The epilogue will tell you why you should read the book. The book is technical. It is more than worth the effort to follow it.
To follow the mathematics you need a thorough grip on basic probability theory. That is, reasoning using conditional probabilities, conjunctions, independent variables, confounding variables - that sort of thing. You also need the basics of graph theory. You really need to be comfortable with these. The reasoning is very sophisticated, even though the mathematics is basic. It is helpful (but not essential) to know the following too: symbolic logic, basic statistics, some Macroeconomics, some computer science and (occasionally) a little vector algebra.
If you have basic probability and know what a graph is, you ought to read the book.

If you read the first edition:
The second edition repeats the first edition verbatim, but at the end of most chapters there's a clearly defined section dealing with subsequent developments. There's a long chapter at the end that updates you on the replies to the first edition, and some helpful new material explaining things (like d-separation) that were tricky the first time through. Some of this is on the author's website too. The updates are concise. Replies to philosophers (at least) are ultimately devastating, although Pearl could explain himself more fully.
I am a philosopher of science.

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Most important book

ByMarco Liuzzion June 2, 2014

Format: HardcoverVerified Purchase

Most important book which illustrates the progresses made in the theory of causality, maybe one of the branches of statistics which developed most in the last few decades, and in which most important progresses were made. Sometimes the exposition of the theories in the book is difficult (also for the technical, experienced reader), and I think the author could do more in this respect, also correcting some logical mistakes.

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There is no better book on Causality

ByJ. M.on September 19, 2014

Format: Kindle EditionVerified Purchase

This book should be on your desk until you finish reading it, then place it in a convenient spot on your shelf. There is no better book on Causality.

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Classic and fundamental

ByHairy Larryon November 7, 2012

Format: HardcoverVerified Purchase

This is the fundamental book that describes a probabilistic approach to causal modeling. This approach has flaws; however many find it useful. Even if someone eventually uses a different approach to causality, they should understand the fundamental approach described here.

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Interesting to say the least

ByOlafuron March 5, 2012

Format: HardcoverVerified Purchase

Being a student of science and engineering, I wonder why this isn't taught alongside probability and statistics as a mandatory course.
It surely gives a better insight into causality than linear regression analysis!

0Comment 6 people found this helpful. Was this review helpful to you?YesNo

Most recent customer reviews

An Occam Razor mind.

Pearl has an Occam Razor mind (that's the best metaphor). Please be sure to listen to a 30-min. interview he gives at Edge(dot)org.

Published 10 months ago by Seattle Socrates

Five Stars

Great book

Published 1 year ago by Amazon Customer

Math too small in Kindle for Android

I will have to get the paper version because the Kindle version on my Android device gets the math in a microscopic font that can't be adjusted.Read more

Published 1 year ago by Tim Freeman

Four Stars

Imperative

Published 1 year ago by Steven Endres

DON'T BUY THE KINDLE EDITION. In the kindle paperwhite ...

DON'T BUY THE KINDLE EDITION. In the kindle paperwhite format, the equation numbers are outside the margins and are unreadable. You cannot zoom the text size of the equations.Read more

Published on May 13, 2015 by elc6

Two Stars

If you want to learn about what causality is, DON'T buy this book.

Published on October 12, 2014 by Gaius

The only good part of Pearl's book is the epilogue

The only good part of Pearl's book is the epilogue, which is a self-standing lecture. Within the book itself, Pearl commingles incompatible ideas about probability, Bayesian...Read more

Published on July 25, 2014 by S. Holtzman

It is a fallacy of wisdom

There are many history stories. There are some new concepts to me. However, it is not good enough to add SKA. (Skill/Knowledge/Attitude)

Published on July 1, 2014 by Amazon Customer

Too basic and unoriginal

I ordered it based on reviews, however was very disappointed and returned it within a week. There are lots of good books on this topic.

Published on May 16, 2014 by SA

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