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Causality: Models, Reasoning, and Inference
 
 

Causality: Models, Reasoning, and Inference (Hardcover)

by Judea Pearl (Author) "Causality connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity ..." (more)
3.0 out of 5 stars  See all reviews (1 customer review)
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Product details

  • Hardcover: 384 pages
  • Publisher: Cambridge University Press (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.co.uk Sales Rank: 219,976 in Books (See Bestsellers in Books)
  • See Complete Table of Contents

Product Description

Review

‘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


Product Description

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.

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Causality connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity. Read the first page
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3 of 22 people found the following review helpful:
3.0 out of 5 stars Academic, 6 Mar 2006
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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