Causality: statistical perspectives and applications

This book looks at a broad collection of contributions from experts in their fields. Providing a thorough treatment on statistical causality. Methods and their applications are provided with theoretical background and emphasis is given to practice rather than theory, with technical content kept to a minimum. Step-by-step instructions for using the methods are presented with a broad range of examples, including medicine, biology, economics, sociology and politicalscience. INDICE: Preface (editors)1 Statistical Causality: Some Historical RemarksAUTHOR(S): D.R. Cox2 The Language of Potential OutcomesAUTHOR(S): Arvid Sjolander3 Structural Equations, Graphs and InterventionsAUTHOR(S): Ilya Shpitser4 The Decision-Theoretic Approach to Causal InferenceAUTHOR(S): A. Philip Dawid5 Causal Inference as a Prediction Problem: Assumptions, Identification, and Evidence SynthesisAUTHOR(S): Sander Greenland6 Graph-Based Criteria of Identifiability of Causal QuestionsAUTHOR(S): Ilya Shpitser7 Causal inference from observational data: a Bayesian predictive approachAUTHOR(S): Elja Arjas8 Causal Inference from Observing Sequences of Actions9 Causal Effects and Natural Laws: towards a Conceptualization of Causal Counterfactuals for Non-Manipulable Exposures, with Application to the Effects of Race and SexAUTHOR(S): Tyler J. VanderWeele and Miguel A. Hernan10 Cross-Classifications by Joint Potential OutcomesAUTHOR(S): Arvid Sjolander11 Estimation of Direct and Indirect EffectsAUTHOR(S): Stijn Vansteelandt12 The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear ModelsAUTHOR(S): Judea Pearl13 The Sufficient CauseFramework in Statistics, Philosophy and the Biomedical and Social SciencesAUTHOR(S): Tyler J. VanderWeele14 Inference about Biological Mechanism on the Basis of Epidemiological DataAUTHOR(S): Carlo Berzuini and A. Philip Dawid15 Ion Channels and Multiple SclerosisAUTHOR(S): Luisa Bernardinelli, Carlo Berzuini,Luisa Foco and Roberta Pastorino16 Supplementary Variables For Causal EstimationAUTHOR(S): Roland R. Ramsahai17 Time-Varying Confounding: Some Practical Considerations in a Likelihood FrameworkAUTHOR(S): Rhian Daniel, Bianca De Stavola and Simon Cousens18 Natural Experiments as a Means of Testing Causal InferencesAUTHOR(S): Michael Rutter19 Nonreactive and Purely Reactive Doses in Observational StudiesAUTHOR(S): Paul R. Rosenbaum20 Evaluation of PotentialMediators in Randomized Trials of Complex Interventions(Psychotherapies)AUTHOR(S): Richard Emsley and Graham Dunn21 Causal Inference in Clinical TrialsAUTHOR(S): Krista Fischer and Ian R. White22 Granger Causality and Causal Inference in TimeSeries AnalysisAUTHOR(S): Michael Eichler23 Dynamic Molecular Networks and Mechanisms in the Biosciences: A StatisticalFrameworkAUTHOR(S): Clive G. BowsherIndex

• ISBN: 978-0-470-66556-5
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 384
• Fecha Publicación: 04/05/2012
• Nº Volúmenes: 1
• Idioma: Inglés