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  <titleInfo>
    <title>Causality : statistical perspectives and applications</title>
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  <name type="personal">
    <namePart>Berzuini, Carlo.</namePart>
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  <name type="personal">
    <namePart>Dawid, Philip.</namePart>
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  <name type="personal">
    <namePart>Bernardinelli, Luisa.</namePart>
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  <genre authority="">Electronic books.</genre>
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    <publisher>Wiley</publisher>
    <dateIssued>2012</dateIssued>
    <issuance>monographic</issuance>
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  <abstract>"This book looks at a broad collection of contributions from experts in their fields"--</abstract>
  <tableOfContents>Statistical causality : some historical remarks -- The language of potential outcomes -- Structural equations, graphs and interventions -- The decision-theoretic approach to causal -- Causal inference as a prediction problem : assumptions, identification, and evidence synthesis -- Graph-based criteria of identifiability of causal questions -- Causal inference from observational data : a Bayesian predictive approach -- Causal inference from observing sequences of actions -- Causal effects and natural laws : towards a conceptualization of causal counterfactuals -- For non-manipulable exposures, with application to the effects of race and sex -- Cross-classifications by joint potential outcomes -- Estimation of direct and indirect effects -- The mediation formula : a guide to the assessment of causal pathways in nonlinear models -- The sufficient cause framework in statistics, philosophy and the biomedical and social sciences -- Inference about biological mechanism on the basis of epidemiological data -- Ion channels and multiple sclerosis -- Supplementary variables for causal estimation -- Time-varying confounding : some practical considerations in a likelihood framework -- Natural experiments as a means of testing causal inferences -- Nonreactive and purely reactive doses in observational studies -- Evaluation of potential mediators in randomized trials of complex interventions (psychotherapies) -- Causal inference in clinical trials -- Granger causality and causal inference in time series analysis -- Dynamic molecular networks and mechanisms iIn the biosciences : a statistical framework.</tableOfContents>
  <note type="statement of responsibility">Carlo Berzuini, Philip Dawid, Luisa Bernardinelli.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Estimation theory</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Causation</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Causality (Physics)</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>MATHEMATICS</topic>
    <topic>Probability &amp; Statistics</topic>
    <topic>General</topic>
  </subject>
  <subject authority="fast">
    <topic>Causality (Physics)</topic>
  </subject>
  <subject authority="fast">
    <topic>Causation</topic>
  </subject>
  <subject authority="fast">
    <topic>Estimation theory</topic>
  </subject>
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