Causality: Models, Reasoning, and Inference

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Cambridge University Press, 2000 - Philosophy - 384 pages
4 Reviews
Written by one of the pre-eminent 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. 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, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable. Professor of Computer Science at the UCLA, Judea Pearl is the winner of the 2008 Benjamin Franklin Award in Computers and Cognitive Science.
  

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Judea Pearl's book Causality Models ,Reasoning and Inference starts with the Theory of Probability and explores the cause and effect Theories of science models. The Probability Theory combines a Predictive and a diagnostic approach , and we , Pathologists are applying just that everyday in our Professional life.Therefore ,I can tell the practical application of such theories using odds and likelihood ratio parameters. A good theory makes a good practice.The dictates of the approach is that the overall strength of a belief in any Hypothesis, is based on our previous experience /knowledge about it + the observed evidence (data collected and analysed as statistical proof). It is a product of 2 factors .
1 The prior odds .This is the predictive and prospective factor provided by our previous/background knowledge about it.
2. The likelihood ratio which gives the diagnostic or retrospective aspect .This is the evidence by observed data in a particular case/situation.The random variables and our expectations (our mathematical models/predictions) are then compared. In Indian Astronomy this prediction, then observation and correction of mathematically derived and observed data is what is called Beejaganitham. In Pathology , we do this in every case when we diagnose,predict prognosis and then wait for the outcome (The follow up). Thus putting theory into practice is what Causality :Models ,Reasoning and Inference means. And a person's belief comes from these factors. Faith and belief of a scientist (whether it is in God or any other subject) come only from these parameters and their careful study.
A good book for Mathematicians and Nonmathematicians alike.
Dr Suvarna Nalapat
 

Review: Causality: Models, Reasoning, and Inference

User Review  - Moshe - Goodreads

You really can infer causation from correlation (with a few caveats). Read full review

Contents

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About the author (2000)

Judea Pearl is professor of computer science and statistics at the University of California, Los Angeles, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human reasoning, and philosophy of science. The author of Heuristics and Probabilistic Reasoning, he is a member of the National Academy of Engineering and a Founding Fellow of the American Association for Artificial Intelligence. Dr Pearl is the recipient of the IJCAI Research Excellence Award for 1999, the London School of Economics Lakatos Award for 2001, and the ACM Alan Newell Award for 2004. In 2008, he received the Franklin Medal for computer and cognitive science from the Franklin Institute.

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