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Causation, Prediction, and Search, Second Edition (Adaptive Computation and Machine Learning) second edition Edition

by
Peter Spirtes (Author)
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Peter Spirtes (Author), Clark Glymour (Author), Richard Scheines (Author)
4.5 out of 5 stars 2 customer reviews
ISBN-13: 978-0262194402
ISBN-10: 0262194406
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Product Details

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 568 pages
  • Publisher: A Bradford Book; second edition edition (January 8, 2001)
  • Language: English
  • ISBN-10: 0262194406
  • ISBN-13: 978-0262194402
  • Product Dimensions: 7 x 1.5 x 9 inches
  • Shipping Weight: 2.6 pounds
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #1,850,187 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

4.0 out of 5 starsone of the most important books on causality
By LOV on December 11, 2010
Format: Hardcover Verified Purchase
This is no doubt one of the key books on causality. The authors developed various search algorithms to determine structure and directions of a causal diagram. They programmed them in their software Tetrad and this book is about the techniques. This one in conjunction with Judea Pearl's Causality are the two most important texts on causality using Bayesian networks/AI. However, I have found Pearl's book more structured and easier to understand than this - partially because of the printing and formatting quality and partially because of the flow.
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5.0 out of 5 starsA book mainly concerned with Causal Discovery
By The Statistician on October 12, 2012
Format: Hardcover
The authors of this book are arguably among the pioneers of the causality discovery topic. The book is a well written, precise, and clear. In comparison with Pearl's book, this book is mainly concerned with causality discovery rather than coping with spurious causation. Note that many algorithms have been developed during the decade after publication of it; so it mainly serves as establishing the foundations of causality discovery. The audience interested in causality discovery algorithms must check the recent literature.
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