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The Art of Causal Conjecture

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In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy.The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the ...

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Overview

In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy.The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences--medicine, business, engineering, and artificial intelligence--must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences.Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated,
and confronted by statistical evidence.

The MIT Press

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What People Are Saying

From the Publisher

"Causality plays an important role in many fields, from engineering to medicine to artificial intelligence. Glenn Shafer has written an important,
scholarly study of causality. He starts with a novel foundation for probability that is mathematically and philosophically solid, uses it to distinguish carefully between notions of causality—all of which have played an important role in the literature—and shows how each can be discovered from evidence. This is a book that will be of interest to those interested in foundational questions of statistics and philosophy, as well as in practical applications of causality."
Joseph Y. Halpern, Professor, Computer Science Department, Cornell
University

The MIT Press

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Product Details

  • ISBN-13: 9780262193689
  • Publisher: MIT Press
  • Publication date: 11/15/1996
  • Series: Artificial Intelligence Series
  • Pages: 531
  • Product dimensions: 8.10 (w) x 9.10 (h) x 1.50 (d)

Table of Contents

Series Foreword
Preface
Acknowledgments
1 Introduction 1
2 Event Trees 31
3 Probability Trees 63
4 The Meaning of Probability 91
5 Independent Events 113
6 Events Tracking Events 135
7 Events as Signs of Events 153
8 Independent Variables 167
9 Variables Tracking Variables 189
10 Variables as Signs of Variables 215
11 An Abstract Theory of Event Trees 229
12 Martingale Trees 247
13 Refining 275
14 Principles of Causal Conjecture 299
15 Causal Models 331
16 Representing Probability Trees 359
App. A Huygens's Probability Trees 379
App. B Some Elements of Graph Theory 385
App. C Some Elements of Order Theory 393
App. D The Sample-Space Framework for Probability 399
App. E Prediction in Probability Spaces 409
App. F Sample-Space Concepts of Independence 425
App. G Prediction Diagrams 453
App. H Abstract Stochastic Processes 477
Glossary of Notation 485
References 491
Index 501
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