Causal Asymmetries

AvDaniel M. Hausman,Brian Skyrms

Inbunden, Engelska, 1998

Del i serien Cambridge Studies in Probability, Induction and Decision Theory

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Beskrivning

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.

Produktinformation

Utgivningsdatum:1998-07-28
Mått:152 x 229 x 22 mm
Vikt:640 g
Format:Inbunden
Språk:Engelska
Serie:Cambridge Studies in Probability, Induction and Decision Theory
Antal sidor:320
Förlag:Cambridge University Press
ISBN:9780521622899

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Recensioner i media

'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

Innehållsförteckning

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.