Causal Asymmetries

Causal Asymmetries

by Daniel M. Hausman
     
 

ISBN-10: 0521622891

ISBN-13: 9780521622899

Pub. Date: 05/28/2004

Publisher: Cambridge University Press

Causation is asymmetrical in many different ways. Causes precede effects; explanations cite causes not effects. Agents use causes to manipulate their effects; they don't use effects to manipulate their causes. Effects of a common cause are correlated; causes of a common effect are not. This book by one of the preeminent philosophers of science writing today offers

Overview

Causation is asymmetrical in many different ways. Causes precede effects; explanations cite causes not effects. Agents use causes to manipulate their effects; they don't use effects to manipulate their causes. Effects of a common cause are correlated; causes of a common effect are not. This book by one of the preeminent philosophers of science writing today offers the most comprehensive account available of causal asymmetries. It is a major book for philosophers of science that will also prove insightful to economists and statisticians.

Product Details

ISBN-13:
9780521622899
Publisher:
Cambridge University Press
Publication date:
05/28/2004
Series:
Cambridge Studies in Probability, Induction and Decision Theory Series
Pages:
320
Product dimensions:
5.98(w) x 8.98(h) x 0.87(d)

Table of Contents

List of figures; Acknowledgements; Introduction: causation and its asymmetries; 1. Metaphysical pictures and wishes; 1*. Transfer theories; 2. Is causation a relation among events?; 3. Causation, regularities and time: Hume's theory; 4. Causation and independence; 4*. Causation, independence and causal connection; 5. Agency theory; 5*. Causal generalizations and agency; 6. The counterfactual theory; 6*. Independence and counterfactual dependence; 7. Counterfactuals, agency and independence; 7*. Agency, counterfactuals and independence; 8. Causation, explanation and laws; 8*. Causation, explanation and independent alterability; 9. Probabilistic causation; 10. Causation and conditional probabilities; 10*. Causal graphs and conditional probabilistic dependencies; 11. Intervention, robustness and probabilistic dependence; 11*. Interventions and conditional probabilities; 12. Operationalizing and revising the independence theory; 12*. Probability distributions and causation; 13. Complications and conclusions; Appendix A: alphabetical list of propositions; Appendix B: list of theorems; References; Index.

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