Return to home page
Searching: Muskingum library catalog
Some OPAL libraries remain closed or are operating at reduced service levels. Materials from those libraries may not be requestable; requested items may take longer to arrive. Note that pickup procedures may differ between libraries. Please contact your library for new procedures, specific requests, or other assistance.
  Previous Record Previous Item Next Item Next Record
  Reviews, Summaries, etc...
EBOOK
Author Berzuini, Carlo.
Title Causality : statistical perspectives and applications / Carlo Berzuini, Philip Dawid, Luisa Bernardinelli.
Imprint Hoboken, N.J. : Wiley, 2012.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK WILEY EBOOKS    ONLINE  
View online
LOCATION CALL # STATUS MESSAGE
 OHIOLINK WILEY EBOOKS    ONLINE  
View online
Author Berzuini, Carlo.
Series Wiley series in probability and statistics
Wiley series in probability and statistics.
Subject Estimation theory.
Causation.
Causality (Physics)
Alt Name Dawid, Philip.
Bernardinelli, Luisa.
Description 1 online resource.
Summary "This book looks at a broad collection of contributions from experts in their fields"-- Provided by publisher.
Bibliography Note Includes bibliographical references and index.
Contents 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.
Machine generated contents note: 1. Statistical causality: Some historical remarks / D.R. Cox -- 1.1. Introduction -- 1.2. Key issues -- 1.3. Rothamsted view -- 1.4. earlier controversy and its implications -- 1.5. Three versions of causality -- 1.6. Conclusion -- References -- 2. language of potential outcomes / Arvid Sjolander -- 2.1. Introduction -- 2.2. Definition of causal effects through potential outcomes -- 2.2.1. Subject-specific causal effects -- 2.2.2. Population causal effects -- 2.2.3. Association versus causation -- 2.3. Identification of population causal effects -- 2.3.1. Randomized experiments -- 2.3.2. Observational studies -- 2.4. Discussion -- References -- 3. Structural equations, graphs and interventions / Ilya Shpitser -- 3.1. Introduction -- 3.2. Structural equations, graphs, and interventions -- 3.2.1. Graph terminology -- 3.2.2. Markovian models -- 3.2.3. Latent projections and semi-Markovian models -- 3.2.4. Interventions in semi-Markovian models -- 3.2.5. Counterfactual distributions in NPSEMs -- 3.2.6. Causal diagrams and counterfactual independence -- 3.2.7. Relation to potential outcomes -- References -- 4. decision-theoretic approach to causal inference / Philip Dawid -- 4.1. Introduction -- 4.2. Decision theory and causality -- 4.2.1. simple decision problem -- 4.2.2. Causal inference -- 4.3. No confounding -- 4.4. Confounding -- 4.4.1. Unconfounding -- 4.4.2. Nonconfounding -- 4.4.3. Back-door formula -- 4.5. Propensity analysis -- 4.6. Instrumental variable -- 4.6.1. Linear model -- 4.6.2. Binary variables -- 4.7. Effect of treatment of the treated -- 4.8. Connections and contrasts -- 4.8.1. Potential responses -- 4.8.2. Causal graphs -- 4.9. Postscript -- Acknowledgements -- References -- 5. Causal inference as a prediction problem: Assumptions, identification and evidence synthesis / Sander Greenland -- 5.1. Introduction -- 5.2. brief commentary on developments since 1970 -- 5.2.1. Potential outcomes and missing data -- 5.2.2. prognostic view -- 5.3. Ambiguities of observational extensions -- 5.4. Causal diagrams and structural equations -- 5.5. Compelling versus plausible assumptions, models and inferences -- 5.6. Nonidentification and the curse of dimensionality -- 5.7. Identification in practice -- 5.8. Identification and bounded rationality -- 5.9. Conclusion -- Acknowledgments -- References -- 6. Graph-based criteria of identifiability of causal questions / Ilya Shpitser -- 6.1. Introduction -- 6.2. Interventions from observations -- 6.3. back-door criterion, conditional ignorability, and covariate adjustment -- 6.4. front-door criterion -- 6.5. Do-calculus -- 6.6. General identification -- 6.7. Dormant independences and post-truncation constraints -- References -- 7. Causal inference from observational data: A Bayesian predictive approach / Elja Arjas -- 7.1. Background -- 7.2. model prototype -- 7.3. Extension to sequential regimes -- 7.4. Providing a causal interpretation: Predictive inference from data -- 7.5. Discussion -- Acknowledgement -- References -- 8. Assessing dynamic treatment strategies / Vanessa Didelez -- 8.1. Introduction -- 8.2. Motivating example -- 8.3. Descriptive versus causal inference -- 8.4. Notation and problem definition -- 8.5. HIV example continued -- 8.6. Latent variables -- 8.7. Conditions for sequential plan identifiability -- 8.7.1. Stability -- 8.7.2. Positivity -- 8.8. Graphical representations of dynamic plans -- 8.9. Abdominal aortic aneurysm surveillance -- 8.10. Statistical inference and computation -- 8.11. Transparent actions -- 8.12. Refinements -- 8.13. Discussion -- Acknowledgements -- References -- 9. Causal effects and natural laws: Towards a conceptualization of causal counterfactuals for nonmanipulable exposures, with application to the effects of race and sex / Miguel A. Hernan -- 9.1. Introduction -- 9.2. Laws of nature and contrary to fact statements -- 9.3. Association and causation in the social and biomedical sciences -- 9.4. Manipulation and counterfactuals -- 9.5. Natural laws and causal effects -- 9.6. Consequences of randomization -- 9.7. On the causal effects of sex and race -- 9.8. Discussion -- Acknowledgements -- References -- 10. Cross-classifications by joint potential outcomes / Arvid Sjolander -- 10.1. Introduction -- 10.2. Bounds for the causal treatment effect in randomized trials with imperfect compliance -- 10.3. Identifying the compiler causal effect in randomized trials with imperfect compliance -- 10.4. Defining the appropriate causal effect in studies suffering from truncation by death -- 10.5. Discussion -- References -- 11. Estimation of direct and indirect effects / Stijn Vansteelandt -- 11.1. Introduction -- 11.2. Identification of the direct and indirect effect -- 11.2.1. Definitions -- 11.2.2. Identification -- 11.3. Estimation of controlled direct effects -- 11.3.1. G-computation -- 11.3.2. Inverse probability of treatment weighting -- 11.3.3. G-estimation for additive and multiplicative models -- 11.3.4. G-estimation for logistic models -- 11.3.5. Case-control studies -- 11.3.6. G-estimation for additive hazard models -- 11.4. Estimation of natural direct and indirect effects -- 11.5. Discussion -- Acknowledgements -- References -- 12. mediation formula: A guide to the assessment of causal pathways in nonlinear models / Judea Pearl -- 12.1. Mediation: Direct and indirect effects -- 12.1.1. Direct versus total effects -- 12.1.2. Controlled direct effects -- 12.1.3. Natural direct effects -- 12.1.4. Indirect effects -- 12.1.5. Effect decomposition -- 12.2. mediation formula: A simple solution to a thorny problem -- 12.2.1. Mediation in nonparametric models -- 12.2.2. Mediation effects in linear, logistic, and probit models -- 12.2.3. Special cases of mediation models -- 12.2.4. Numerical example -- 12.3. Relation to other methods -- 12.3.1. Methods based on differences and products -- 12.3.2. Relation to the principal-strata direct effect -- 12.4. Conclusions -- Acknowledgments -- References -- 13. sufficient cause framework in statistics, philosophy and the biomedical and social sciences / Tyler J. VanderWeele -- 13.1. Introduction -- 13.2. sufficient cause framework in philosophy -- 13.3. sufficient cause framework in epidemiology and biomedicine -- 13.4. sufficient cause framework in statistics -- 13.5. sufficient cause framework in the social sciences -- 13.6. Other notions of sufficiency and necessity in causal inference -- 13.7. Conclusion -- Acknowledgements -- References -- 14. Analysis of interaction for identifying causal mechanisms / Miles Parkes -- 14.1. Introduction -- 14.2. What is a mechanism-- 14.3. Statistical versus mechanistic interaction -- 14.4. Illustrative example -- 14.5. Mechanistic interaction defined -- 14.6. Epistasis -- 14.7. Excess risk and superadditivity -- 14.8. Conditions under which excess risk and superadditivity indicate the presence of mechanistic interaction -- 14.9. Collapsibility -- 14.10. Back to the illustrative study -- 14.11. Alternative approaches -- 14.12. Discussion -- Ethics statement -- Financial disclosure -- References -- 15. Ion channels as a possible mechanism of neurodegeneration in multiple sclerosis / Roberta Pastorino -- 15.1. Introduction -- 15.2. Background -- 15.3. scientific hypothesis -- 15.4. Data -- 15.5. simple preliminary analysis -- 15.6. Testing for qualitative interaction -- 15.7. Discussion -- Acknowledgments -- References -- 16. Supplementary variables for causal estimation / Roland R. Ramsahai -- 16.1. Introduction -- 16.2. Multiple expressions for causal effect -- 16.3. Asymptotic variance of causal estimators -- 16.4. Comparison of causal estimators -- 16.4.1. Supplement C with L or not -- 16.4.2. Supplement L with C or not -- 16.4.3. Replace C with L or not -- 16.5. Discussion -- Acknowledgements -- Appendices -- 16.A. Estimator given all X's recorded -- 16.B. Derivations of asymptotic variances -- 16.C. Expressions with correlation coefficients -- 16.D. Derivation of ΔII's -- 16.E. Relation between ρ2rl/t and ρ2rl/c -- References -- 17. Time-varying confounding: Some practical considerations in a likelihood framework / Simon Cousens -- 17.1. Introduction -- 17.2. General setting -- 17.2.1. Notation -- 17.2.2. Observed data structure -- 17.2.3. Intervention strategies -- 17.2.4. Potential outcomes -- 17.2.5. Time-to-event outcomes -- 17.2.6. Causal estimands -- 17.3. Identifying assumptions -- 17.4. G-computation formula -- 17.4.1. formula -- 17.4.2. Plug-in regression estimation -- 17.5. Implementation by Monte Carlo simulation -- 17.5.1. Simulating an end-of-study outcome -- 17.5.2. Simulating a time-to-event outcome -- 17.5.3. Inference -- 17.5.4. Losses to follow-up -- 17.5.5. Software -- 17.6. Analyses of simulated data -- 17.6.1. data -- 17.6.2. Regimes to be compared -- 17.6.3. Parametric modelling choices -- 17.6.4. Results -- 17.7. Further considerations -- 17.7.1. Parametric model misspecification -- 17.7.2. Competing events -- 17.7.3. Unbalanced measurement times -- 17.8. Summary -- References -- 18. Natural experiments' as a means of testing causal inferences / Michael Rutter -- 18.1. Introduction -- 18.2. Noncausal interpretations of an association.
Note Print version record and CIP data provided by publisher.
ISBN 9781119941736 (epub)
1119941733 (epub)
9781119945703 (pdf)
1119945704 (pdf)
9781119941743 (mobi)
1119941741 (mobi)
9781119945710 (electronic bk.)
1119945712 (electronic bk.)
0470665564
9780470665565
9780470665565 (hardback)
ISBN/ISSN 9786613656162
OCLC # 772611169
Additional Format Print version: Berzuini, Carlo. Causality. Hoboken, N.J. : Wiley, 2012 9780470665565 (DLC) 2011049795


If you experience difficulty accessing or navigating this content, please contact the OPAL Support Team