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Causality: Models, Reasoning and Inference Hardcover – September 14, 2009

ISBN-13: 978-0521895606 ISBN-10: 052189560X Edition: 2nd

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Causality: Models, Reasoning and Inference + Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research) + Mostly Harmless Econometrics: An Empiricist's Companion
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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: 3.9 out of 5 stars  See all reviews (18 customer reviews)
  • Amazon Best Sellers Rank: #28,181 in Books (See Top 100 in Books)

Editorial Reviews


"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

Book Description

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, philosophy, cognitive science, and the health and social sciences.

Customer Reviews

3.9 out of 5 stars
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See all 18 customer reviews
It is not even for most autodidact mathematicians.
Gaetan Lion
This is the fundamental book that describes a probabilistic approach to causal modeling.
Hairy Larry
This is applied to practical examples in a very wide range of fields.
Samuel W. Mitchell

Most Helpful Customer Reviews

86 of 88 people found the following review helpful By Samuel W. Mitchell on January 30, 2011
Format: Hardcover Verified 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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37 of 39 people found the following review helpful By Steve on February 11, 2010
Format: Hardcover Verified 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).
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63 of 74 people found the following review helpful By Gaetan Lion on September 14, 2011
Format: Hardcover Verified Purchase
This book is not for the layperson. It is not even for most autodidact mathematicians. Unless you have a degree in mathematics or you are a professional using advanced mathematics in your daily working life, you probably will experience much frustration reading this book. Reasonably advanced Probability Theory and Bayesian Statistics are two domains that may be extremely helpful in deciphering this book. Without them, I would recommend passing on this one.

For one thing, Judea Pearl frequently uses different math notation descriptions than the ones you are familiar with for such concepts as correlation, covariance, and linear regression among others. Pearl even turns on their heads simple concepts such as "y" stands for the dependent variable and "x" for an independent variable (he treats x very often as the dependent variable; and y sometimes as an independent one). Those obfuscations related to foundational concepts make it difficult for the reader to build knowledge related to Pearl's far more complicated methods.

None of the above detracts from the pioneering quality of Pearl's work on causality.

The above just gives the prospective reader a fair warning whether he is equipped and motivated to tackle such a challenging book. Also, the material could have been presented in a more user friendly way to increase the audience to at least the ones with reasonably good quantitative numeracy without being professional mathematicians.
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15 of 15 people found the following review helpful By B. Paulewicz on April 28, 2013
Format: Kindle Edition Verified 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.Read more ›
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Causality: Models, Reasoning and Inference
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