Causality Kindle Edition

by Judea Pearl (Author)

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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. Cited in more than 2,100 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 interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.

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Product Details

File Size: 5449 KB
Print Length: 484 pages
Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
Publisher: Cambridge University Press; 2nd edition (September 14, 2009)
Publication Date: September 14, 2009
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00AKE1VYK
Text-to-Speech: Enabled

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Amazon Best Sellers Rank: #474,728 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
- #308 in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Applied > Probability & Statistics
- #753 in Kindle Store > Kindle eBooks > Nonfiction > Science > History & Philosophy
- #2073 in Books > Science & Math > Mathematics > Applied > Probability & Statistics

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Top Customer Reviews

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.

11 comment 115 people found this helpful. Was this review helpful to you?YesNo

some interesting questions

ByXYon February 11, 2010

Format: HardcoverVerified Purchase

In the introductory material, the book claims the graphical method presented in this book 'solves' the problem of causality. However, the book does not read as if the problem has been solved. Instead, it reads like an extended discussion/argument with philosophers, scientists, and statisticians. The book raises a great many interesting questions (some it raises only implicitly), so for this reason I give it 5 stars without hesitation. I do recommend, though, that the third edition of this book substantially reorganize the material; for example, the excellent epilogue should be brought forward as introductory material (and expanded).

0Comment 46 people found this helpful. Was this review helpful to you?YesNo

The most important book on the subject, but not necessarily the best introduction

ByB. Paulewiczon April 28, 2013

Format: Kindle EditionVerified Purchase

I am a cognitive psychologist with some modest background in statistics and so I will only say something about the importance of this book to people like me. In psychology, as in many other sciences where a) important causal relations often cannot be tested directly by means of experimental manipulation or b) the validity of experimental manipulation or of the effects measures is often questionable it is essential to understand and use the ideas presented in this groundbreaking book. For example, whenever you perform an experiment there are essentially only a few ways in which your manipulation or your effect's measures can be problematic (with regard to the research question). Knowing exactly how this can happen allows you to find the problem quicker or, even better, find it in advance. In fact, many published experiments are simply attempts to address this kind of issues even though it would probably come as a surprise to the authors of these studies to see that it is the case. Also, in certain areas of psychology, e.g., individual differences or clinical psychology, heavy use is made of mediational analyses, structural models and various almost-but-not-quite experimental designs. One of the shocking and inescapable implications of Pearl's discoveries is that a lot of the conclusions routinely drawn from such studies are simply wrong, for example, the typical way of doing mediational analysis (be it vanilla Baron-Kenny, it's trivial extension to nonlinear settings or Baron-Kenny + bootstrap to compute certain confidence intervals) assumes that the mediator is measured without error but in psychology it is almost allways measured with substantial error - causal analysis let's you discover how exactly this affects the validity of the conclusions. I wont even try to begin to explain in what ways the structural equation models are abused in the majority of papers I've seen. If your are a psychologist than I suppose this might not be the best place to start - I'd recommend going through "Causal inference in statistics" (available from Judea Pearl's website) several times before reading this book.

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Most Recent Customer Reviews

Published 3 months 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.

Published 6 months ago by Amazon Customer

Published 6 months 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.

Published 1 year ago by elc6

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

Published on October 12, 2014 by Gaius

There is no better book on Causality

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.

Published on September 19, 2014 by J. McGuire

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

Most important book

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...Read more

Published on June 2, 2014 by Marco Liuzzi

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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