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Causation, Prediction, and Search, 2nd Edition

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Author Info

  • Peter Spirtes

    ()
    (Carnegie Mellon University)

  • Clark Glymour

    ()
    (Carnegie Mellon University)

  • Richard Scheines

    ()
    (Carnegie Mellon University)

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    Abstract

    What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences. The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables. The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.

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    Bibliographic Info

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    This book is provided by The MIT Press in its series MIT Press Books with number 0262194406 and published in 2001.

    Volume: 1
    Edition: 1
    ISBN: 0-262-19440-6
    Handle: RePEc:mtp:titles:0262194406

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    Web page: http://mitpress.mit.edu

    Related research

    Keywords: Bayes networks; causal models; Simpson's paradox; regressions models;

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    Cited by:
    1. Chen, Pu & Hsiao, Chih-Ying, 2008. "What happens to Japan if China catches a cold?: A causal analysis of Chinese growth and Japanese growth," Japan and the World Economy, Elsevier, vol. 20(4), pages 622-638, December.
    2. Chen, Pu & Chihying, Hsiao, 2007. "Learning Causal Relations in Multivariate Time Series Data," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 1(11), pages 1-43.
    3. Selva Demiralp & Kevin Hoover & Stephen Perez, 2014. "Still puzzling: evaluating the price puzzle in an empirically identified structural vector autoregression," Empirical Economics, Springer, vol. 46(2), pages 701-731, March.
    4. Heinlein, Reinhold & Krolzig, Hans-Martin, 2012. "On the construction of two-country cointegrated VAR models with an application to the UK and US," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62310, Verein für Socialpolitik / German Economic Association.

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