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Causality: Models, Reasoning, and Inference [Hardcover]

Judea Pearl
3.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

13 Mar 2000
Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. 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, artificial intelligence, business, epidemiology, social science and economics.

Product details

  • Hardcover: 400 pages
  • Publisher: Cambridge University Press; Reprinted with corrections edition (13 Mar 2000)
  • Language: English
  • ISBN-10: 0521773628
  • ISBN-13: 978-0521773621
  • Product Dimensions: 25.8 x 18.6 x 3 cm
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 789,254 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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'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

'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 the 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

'The book is extremely well written, and while mathematically precise, provides a thought-provoking study of causality and its implications.' Computing Review

Book Description

Causality offers the first comprehensive coverage of causal analysis in many 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.

Inside This Book (Learn More)
First Sentence
Causality connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most Helpful Customer Reviews
3 of 24 people found the following review helpful
3.0 out of 5 stars Academic 6 Mar 2006
Format:Hardcover|Verified Purchase
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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Most Helpful Customer Reviews on (beta) 3.8 out of 5 stars  13 reviews
58 of 63 people found the following review helpful
5.0 out of 5 stars Pearl summarizes his work on causation. 11 July 2000
By Mikel Aickin - Published on
Judea Pearl and his colleagues at UCLA (and elsewhere) have published a large number of papers and written unpublished reports over the past 15 years, in which they have developed a modern, analytical approach to causation. Many of these are in somewhat obscure publications, and so it is especially helpful to have the most important of them collected together in this volume. Pearl has edited, written new chapters and connecting prose, to weave this summary of a substantial amount of research.
Although the dust-jacket suggests that only modest mathematics is needed, and although this is technically true, it is misleading, because the whole area requires a sophistication of thought that goes well beyond the simplicity of the tools. Nonetheless, there is currently no other volume that is as easy to read as this, and summarizes so much material so compactly.
It is possible that the new vision of causal analysis developed by Spirtes, Scheines, Glymour, Pearl, Robins, Verma, Heckerman, Meek, and others, will have profound effect on how we analyze research data. If so, this book will be necessary reading for decades to come.
36 of 38 people found the following review helpful
5.0 out of 5 stars Pearl's view on causality 22 Feb 2008
By Michael R. Chernick - Published on
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.
25 of 26 people found the following review helpful
5.0 out of 5 stars The best and only on the topic 20 May 2001
By A Customer - Published on
A great text, if for no other reason than the fact that it fills an important niche. Pearl does an excellent job of delineating causal models as both philosophical and statistical problems. I found the coverage of latent variable models particularly useful.
My only complaint is Pearl often makes assumptions without justifying them sufficiently. Usually, the assumptions made are reasonable or of negligible consequence, but at other times, the veracity of the assumptions is arguably core matter of the discussion. The net effect is a feeling of reading a brilliant, detailed exposition of what causal models imply observationally, undermined by doubts about the appropriateness of causality as a concept at all.
Overall, however, this a wonderful text that should be useful to anyone interested in causality or statistical modeling.
31 of 36 people found the following review helpful
5.0 out of 5 stars Important but difficult 15 Sep 2004
By Peter McCluskey - Published on
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.
24 of 27 people found the following review helpful
5.0 out of 5 stars Understanding causality poses no danger! 28 Feb 2001
By "funkylikwid" - Published on
I take issue with the previous reviewer. Pearl does not assume that the modeller is able, a priori, to determine what the correct model is. Instead, Pearl asks what conclusions can be drawn if the modeller is able to substantiate only parts of the model. By systematically changing those parts, he then obtains a full picture of what modeling assumptions "must" be substantiated before causal inferences can be derived from nonexperimental data. An anslysis of assumptions is not a license to abuse them.
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