Causal Asymmetries
£26.99
Part of Cambridge Studies in Probability, Induction and Decision Theory
- Author: Daniel M. Hausman, University of Wisconsin, Madison
- Date Published: February 2008
- availability: Available
- format: Paperback
- isbn: 9780521052429
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This book, by one of the pre-eminent philosophers of science writing today, offers the most comprehensive account available of causal asymmetries. 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 explains why a relationship that is asymmetrical in one of these regards is asymmetrical in the others. Hausman discovers surprising hidden connections between theories of causation and traces them all to an asymmetry of independence. This is a major book for philosophers of science that will also prove insightful to economists and statisticians.
Read more- Main selling point is Hausman, a very well-known philosopher who will be known to philosophers of science and social science, especially economics
- Book is at once a critical introduction to modern theories of causation, and a defence of a new general theory of causation
Reviews & endorsements
'Any serious student of causality - economist or philosopher - will find careful study of Hausman's a valuable tool for clarifying his or her own thinking about causality.' Journal of Economic Methodology
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×Product details
- Date Published: February 2008
- format: Paperback
- isbn: 9780521052429
- length: 320 pages
- dimensions: 228 x 153 x 18 mm
- weight: 0.49kg
- contains: 34 b/w illus.
- availability: Available
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.
also by this author
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