Overview
- Authors:
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Peter Spirtes
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Department of Philosophy, Carnegie Mellon University, Pittsburgh, USA
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Clark Glymour
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Department of Philosophy, Carnegie Mellon University, Pittsburgh, USA
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Richard Scheines
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Department of Philosophy, Carnegie Mellon University, Pittsburgh, USA
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Table of contents (13 chapters)
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Front Matter
Pages i-xxiii
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 1-24
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 25-40
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 41-86
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 87-102
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 103-162
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 163-200
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 201-237
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 238-258
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 259-305
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 306-322
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 323-353
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 354-366
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- Peter Spirtes, Clark Glymour, Richard Scheines
Pages 367-480
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Back Matter
Pages 481-529
About this book
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.
