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Causality : statistical perspectives and applications /
"This book looks at a broad collection of contributions from experts in their fields"--
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex. :
Wiley,
2012.
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| Colección: | Wiley series in probability and statistics.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Berzuini, Carlo. | |
| 245 | 1 | 0 | |a Causality : |b statistical perspectives and applications / |c Carlo Berzuini, Philip Dawid, Luisa Bernardinelli. |
| 264 | 1 | |a Chichester, West Sussex. : |b Wiley, |c 2012. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Wiley series in probability and statistics | |
| 520 | |a "This book looks at a broad collection of contributions from experts in their fields"-- |c Provided by publisher. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a 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. | |
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Estimation theory. | |
| 650 | 0 | |a Causation. | |
| 650 | 0 | |a Causality (Physics) | |
| 650 | 6 | |a Théorie de l'estimation. | |
| 650 | 6 | |a Causalité (Physique) | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Causality (Physics) |2 fast | |
| 650 | 7 | |a Causation |2 fast | |
| 650 | 7 | |a Estimation theory |2 fast | |
| 700 | 1 | |a Dawid, Philip. | |
| 700 | 1 | |a Bernardinelli, Luisa. | |
| 776 | 0 | 8 | |i Print version: |a Berzuini, Carlo. |t Causality. |d Hoboken, N.J. : Wiley, 2012 |z 9780470665565 |w (DLC) 2011049795 |
| 830 | 0 | |a Wiley series in probability and statistics. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=927597 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1. |t Statistical causality: Some historical remarks / |r D.R. Cox -- |g 1.1. |t Introduction -- |g 1.2. |t Key issues -- |g 1.3. |t Rothamsted view -- |g 1.4. |t earlier controversy and its implications -- |g 1.5. |t Three versions of causality -- |g 1.6. |t Conclusion -- |t References -- |g 2. |t language of potential outcomes / |r Arvid Sjolander -- |g 2.1. |t Introduction -- |g 2.2. |t Definition of causal effects through potential outcomes -- |g 2.2.1. |t Subject-specific causal effects -- |g 2.2.2. |t Population causal effects -- |g 2.2.3. |t Association versus causation -- |g 2.3. |t Identification of population causal effects -- |g 2.3.1. |t Randomized experiments -- |g 2.3.2. |t Observational studies -- |g 2.4. |t Discussion -- |t References -- |g 3. |t Structural equations, graphs and interventions / |r Ilya Shpitser -- |g 3.1. |t Introduction -- |g 3.2. |t Structural equations, graphs, and interventions -- |g 3.2.1. |t Graph terminology -- |g 3.2.2. |t Markovian models -- |g 3.2.3. |t Latent projections and semi-Markovian models -- |g 3.2.4. |t Interventions in semi-Markovian models -- |g 3.2.5. |t Counterfactual distributions in NPSEMs -- |g 3.2.6. |t Causal diagrams and counterfactual independence -- |g 3.2.7. |t Relation to potential outcomes -- |t References -- |g 4. |t decision-theoretic approach to causal inference / |r Philip Dawid -- |g 4.1. |t Introduction -- |g 4.2. |t Decision theory and causality -- |g 4.2.1. |t simple decision problem -- |g 4.2.2. |t Causal inference -- |g 4.3. |t No confounding -- |g 4.4. |t Confounding -- |g 4.4.1. |t Unconfounding -- |g 4.4.2. |t Nonconfounding -- |g 4.4.3. |t Back-door formula -- |g 4.5. |t Propensity analysis -- |g 4.6. |t Instrumental variable -- |g 4.6.1. |t Linear model -- |g 4.6.2. |t Binary variables -- |g 4.7. |t Effect of treatment of the treated -- |g 4.8. |t Connections and contrasts -- |g 4.8.1. |t Potential responses -- |g 4.8.2. |t Causal graphs -- |g 4.9. |t Postscript -- |t Acknowledgements -- |t References -- |g 5. |t Causal inference as a prediction problem: Assumptions, identification and evidence synthesis / |r Sander Greenland -- |g 5.1. |t Introduction -- |g 5.2. |t brief commentary on developments since 1970 -- |g 5.2.1. |t Potential outcomes and missing data -- |g 5.2.2. |t prognostic view -- |g 5.3. |t Ambiguities of observational extensions -- |g 5.4. |t Causal diagrams and structural equations -- |g 5.5. |t Compelling versus plausible assumptions, models and inferences -- |g 5.6. |t Nonidentification and the curse of dimensionality -- |g 5.7. |t Identification in practice -- |g 5.8. |t Identification and bounded rationality -- |g 5.9. |t Conclusion -- |t Acknowledgments -- |t References -- |g 6. |t Graph-based criteria of identifiability of causal questions / |r Ilya Shpitser -- |g 6.1. |t Introduction -- |g 6.2. |t Interventions from observations -- |g 6.3. |t back-door criterion, conditional ignorability, and covariate adjustment -- |g 6.4. |t front-door criterion -- |g 6.5. |t Do-calculus -- |g 6.6. |t General identification -- |g 6.7. |t Dormant independences and post-truncation constraints -- |t References -- |g 7. |t Causal inference from observational data: A Bayesian predictive approach / |r Elja Arjas -- |g 7.1. |t Background -- |g 7.2. |t model prototype -- |g 7.3. |t Extension to sequential regimes -- |g 7.4. |t Providing a causal interpretation: Predictive inference from data -- |g 7.5. |t Discussion -- |t Acknowledgement -- |t References -- |g 8. |t Assessing dynamic treatment strategies / |r Vanessa Didelez -- |g 8.1. |t Introduction -- |g 8.2. |t Motivating example -- |g 8.3. |t Descriptive versus causal inference -- |g 8.4. |t Notation and problem definition -- |g 8.5. |t HIV example continued -- |g 8.6. |t Latent variables -- |g 8.7. |t Conditions for sequential plan identifiability -- |g 8.7.1. |t Stability -- |g 8.7.2. |t Positivity -- |g 8.8. |t Graphical representations of dynamic plans -- |g 8.9. |t Abdominal aortic aneurysm surveillance -- |g 8.10. |t Statistical inference and computation -- |g 8.11. |t Transparent actions -- |g 8.12. |t Refinements -- |g 8.13. |t Discussion -- |t Acknowledgements -- |t References -- |g 9. |t Causal effects and natural laws: Towards a conceptualization of causal counterfactuals for nonmanipulable exposures, with application to the effects of race and sex / |r Miguel A. Hernan -- |g 9.1. |t Introduction -- |g 9.2. |t Laws of nature and contrary to fact statements -- |g 9.3. |t Association and causation in the social and biomedical sciences -- |g 9.4. |t Manipulation and counterfactuals -- |g 9.5. |t Natural laws and causal effects -- |g 9.6. |t Consequences of randomization -- |g 9.7. |t On the causal effects of sex and race -- |g 9.8. |t Discussion -- |t Acknowledgements -- |t References -- |g 10. |t Cross-classifications by joint potential outcomes / |r Arvid Sjolander -- |g 10.1. |t Introduction -- |g 10.2. |t Bounds for the causal treatment effect in randomized trials with imperfect compliance -- |g 10.3. |t Identifying the compiler causal effect in randomized trials with imperfect compliance -- |g 10.4. |t Defining the appropriate causal effect in studies suffering from truncation by death -- |g 10.5. |t Discussion -- |t References -- |g 11. |t Estimation of direct and indirect effects / |r Stijn Vansteelandt -- |g 11.1. |t Introduction -- |g 11.2. |t Identification of the direct and indirect effect -- |g 11.2.1. |t Definitions -- |g 11.2.2. |t Identification -- |g 11.3. |t Estimation of controlled direct effects -- |g 11.3.1. |t G-computation -- |g 11.3.2. |t Inverse probability of treatment weighting -- |g 11.3.3. |t G-estimation for additive and multiplicative models -- |g 11.3.4. |t G-estimation for logistic models -- |g 11.3.5. |t Case-control studies -- |g 11.3.6. |t G-estimation for additive hazard models -- |g 11.4. |t Estimation of natural direct and indirect effects -- |g 11.5. |t Discussion -- |t Acknowledgements -- |t References -- |g 12. |t mediation formula: A guide to the assessment of causal pathways in nonlinear models / |r Judea Pearl -- |g 12.1. |t Mediation: Direct and indirect effects -- |g 12.1.1. |t Direct versus total effects -- |g 12.1.2. |t Controlled direct effects -- |g 12.1.3. |t Natural direct effects -- |g 12.1.4. |t Indirect effects -- |g 12.1.5. |t Effect decomposition -- |g 12.2. |t mediation formula: A simple solution to a thorny problem -- |g 12.2.1. |t Mediation in nonparametric models -- |g 12.2.2. |t Mediation effects in linear, logistic, and probit models -- |g 12.2.3. |t Special cases of mediation models -- |g 12.2.4. |t Numerical example -- |g 12.3. |t Relation to other methods -- |g 12.3.1. |t Methods based on differences and products -- |g 12.3.2. |t Relation to the principal-strata direct effect -- |g 12.4. |t Conclusions -- |t Acknowledgments -- |t References -- |g 13. |t sufficient cause framework in statistics, philosophy and the biomedical and social sciences / |r Tyler J. VanderWeele -- |g 13.1. |t Introduction -- |g 13.2. |t sufficient cause framework in philosophy -- |g 13.3. |t sufficient cause framework in epidemiology and biomedicine -- |g 13.4. |t sufficient cause framework in statistics -- |g 13.5. |t sufficient cause framework in the social sciences -- |g 13.6. |t Other notions of sufficiency and necessity in causal inference -- |g 13.7. |t Conclusion -- |t Acknowledgements -- |t References -- |g 14. |t Analysis of interaction for identifying causal mechanisms / |r Miles Parkes -- |g 14.1. |t Introduction -- |g 14.2. |t What is a mechanism-- |g 14.3. |t Statistical versus mechanistic interaction -- |g 14.4. |t Illustrative example -- |g 14.5. |t Mechanistic interaction defined -- |g 14.6. |t Epistasis -- |g 14.7. |t Excess risk and superadditivity -- |g 14.8. |t Conditions under which excess risk and superadditivity indicate the presence of mechanistic interaction -- |g 14.9. |t Collapsibility -- |g 14.10. |t Back to the illustrative study -- |g 14.11. |t Alternative approaches -- |g 14.12. |t Discussion -- |t Ethics statement -- |t Financial disclosure -- |t References -- |g 15. |t Ion channels as a possible mechanism of neurodegeneration in multiple sclerosis / |r Roberta Pastorino -- |g 15.1. |t Introduction -- |g 15.2. |t Background -- |g 15.3. |t scientific hypothesis -- |g 15.4. |t Data -- |g 15.5. |t simple preliminary analysis -- |g 15.6. |t Testing for qualitative interaction -- |g 15.7. |t Discussion -- |t Acknowledgments -- |t References -- |g 16. |t Supplementary variables for causal estimation / |r Roland R. |
| 880 | 0 | 0 | |t Ramsahai -- |g 16.1. |t Introduction -- |g 16.2. |t Multiple expressions for causal effect -- |g 16.3. |t Asymptotic variance of causal estimators -- |g 16.4. |t Comparison of causal estimators -- |g 16.4.1. |t Supplement C with L or not -- |g 16.4.2. |t Supplement L with C or not -- |g 16.4.3. |t Replace C with L or not -- |g 16.5. |t Discussion -- |t Acknowledgements -- |t Appendices -- |g 16.A. |t Estimator given all X's recorded -- |g 16.B. |t Derivations of asymptotic variances -- |g 16.C. |t Expressions with correlation coefficients -- |g 16.D. |t Derivation of ΔII's -- |g 16.E. |t Relation between ρ2rl/t and ρ2rl/c -- |t References -- |g 17. |t Time-varying confounding: Some practical considerations in a likelihood framework / |r Simon Cousens -- |g 17.1. |t Introduction -- |g 17.2. |t General setting -- |g 17.2.1. |t Notation -- |g 17.2.2. |t Observed data structure -- |g 17.2.3. |t Intervention strategies -- |g 17.2.4. |t Potential outcomes -- |g 17.2.5. |t Time-to-event outcomes -- |g 17.2.6. |t Causal estimands -- |g 17.3. |t Identifying assumptions -- |g 17.4. |t G-computation formula -- |g 17.4.1. |t formula -- |g 17.4.2. |t Plug-in regression estimation -- |g 17.5. |t Implementation by Monte Carlo simulation -- |g 17.5.1. |t Simulating an end-of-study outcome -- |g 17.5.2. |t Simulating a time-to-event outcome -- |g 17.5.3. |t Inference -- |g 17.5.4. |t Losses to follow-up -- |g 17.5.5. |t Software -- |g 17.6. |t Analyses of simulated data -- |g 17.6.1. |t data -- |g 17.6.2. |t Regimes to be compared -- |g 17.6.3. |t Parametric modelling choices -- |g 17.6.4. |t Results -- |g 17.7. |t Further considerations -- |g 17.7.1. |t Parametric model misspecification -- |g 17.7.2. |t Competing events -- |g 17.7.3. |t Unbalanced measurement times -- |g 17.8. |t Summary -- |t References -- |g 18. |t Ǹatural experiments' as a means of testing causal inferences / |r Michael Rutter -- |g 18.1. |t Introduction -- |g 18.2. |t Noncausal interpretations of an association. |
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