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Causal asymmetries / Daniel M. Hausman.

By: Material type: TextTextSeries: Cambridge studies in probability, induction, and decision theoryPublisher: Cambridge : Cambridge University Press, 1998Description: 1 online resource (xv, 300 pages) : digital, PDF file(s)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780511663710 (ebook)
Subject(s): Additional physical formats: Print version: : No titleDDC classification:
  • 122 21
LOC classification:
  • BD541 .H38 1998
Online resources:
Contents:
Metaphysical Pictures and Wishes -- Transfer Theories -- Is Causation a Relation Among Events? -- Causation, Regularities, and Time: Hume's Theory -- Causation and Independence -- Causation, Independence, and Causal Connection -- Agency Theory -- Causal Generalizations and Agency -- The Counterfactual Theory -- Independence and Counterfactual Dependence -- Counterfactuals, Agency, and Independence -- Agency, Counterfactuals, and Independence -- Causation, Explanation, and Laws -- Causation, Explanation, and Independent Alterability -- Probabilistic Causation -- Causation and Conditional Probabilities -- Causal Graphs and Conditional Probabilistic Dependencies -- Intervention, Robustness, and Probabilistic Dependence.
Summary: 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.
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Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Metaphysical Pictures and Wishes -- Transfer Theories -- Is Causation a Relation Among Events? -- Causation, Regularities, and Time: Hume's Theory -- Causation and Independence -- Causation, Independence, and Causal Connection -- Agency Theory -- Causal Generalizations and Agency -- The Counterfactual Theory -- Independence and Counterfactual Dependence -- Counterfactuals, Agency, and Independence -- Agency, Counterfactuals, and Independence -- Causation, Explanation, and Laws -- Causation, Explanation, and Independent Alterability -- Probabilistic Causation -- Causation and Conditional Probabilities -- Causal Graphs and Conditional Probabilistic Dependencies -- Intervention, Robustness, and Probabilistic Dependence.

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.

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