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Causality, Probability, and Time by [Kleinberg, Samantha]
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Length: 265 pages

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Product Description


'… informative and engaging … Arguably an equally valuable contribution of the book is its integration of relevant work in philosophy, computer science, and statistics.' David R. Bickel, Mathematical Reviews

Book Description

This book presents a new approach to causal inference (finding relationships from a set of data) and explanation (assessing why a particular event occurred), addressing both the timing and complexity of relationships. The practical use of the method developed is illustrated through theoretical and experimental case studies, demonstrating its feasibility and success.

Product details

  • Format: Kindle Edition
  • File Size: 2274 KB
  • Print Length: 265 pages
  • Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
  • Publisher: Cambridge University Press; 1 edition (12 Nov. 2012)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B00ADP70RI
  • Text-to-Speech: Enabled
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  • Word Wise: Not Enabled
  • Enhanced Typesetting: Not Enabled
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #580,727 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Amazon.com: HASH(0x9bb361bc) out of 5 stars 1 review
6 of 7 people found the following review helpful
HASH(0x9b82269c) out of 5 stars A Temporo-philosophical Primer to Causal Inference with Case Studies 14 Feb. 2013
By Adnan Masood, PhD - Published on Amazon.com
Format: Hardcover
Causality, Probability and Time by Dr. Samantha Kelinberg is a whirlwind yet original journey of the interdisciplinary study of probabilistic temporal logic and causal inference. Probabilistic causation is a fairly demanding area of study which studies the relationship between cause and effect using the tools of probability theory. Judea Pearl, in his seminal text "Causality: Models, Reasoning, and Inference" refers to this quandary by stating that

(causality) connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity.

Dr. Kelinberg's work provides a balanced introduction to background work on this topic while breaking new grounds on a well-positioned approach of causality based on temporal logic. The envisioning problem is the problem of deducing the set of facts, possibly as the result of our actions leading to the decision problem. This is compounded with finding a timely and useful way to represent our knowledge about time, change, and chance. In this ~260 page book, Dr. Kelinberg begins with a brief history of causality leading to Probability, logic and probabilistic temporal logic. The author then defines causality from various different facets, proceeding to causality inference, token causality and then finally the case studies. With practical examples and algorithms, author devises simple mathematical tools for analyzing the relationships between causal connections, inference, causal significance, model complexity, statistical associations, actions and observations.

Exploiting the temporal nature of probabilistic events, Dr. Kelinberg's research is a thought provoking and valuable addition to the scientific community interested in learning causal effects and inference with respect to time. Built upon the works of the likes of Heckerman, Breese, Santos and Young, this book will pave the way probabilistic reasoning researchers think about temporal effects on causality for years to come.

David Hume believed that the causes are invariably followed by their effects: "We may define a cause to be an object, followed by another, and where all the objects similar to the first, are followed by objects similar to the second." So, would you like a well written margin-annotation-laden text which provides formal and practical case study based approach to this somewhat abstract concept of causality? Then look no further!
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