| Description |
1 online resource |
| Series |
Wiley series in probability and statistics |
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Wiley series in probability and statistics.
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| Contents |
880-01 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 |
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880-01/(S 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. Ǹatural experiments' as a means of testing causal inferences / Michael Rutter -- 18.1. Introduction -- 18.2. Noncausal interpretations of an association |
| Summary |
"This book looks at a broad collection of contributions from experts in their fields"-- Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record and CIP data provided by publisher |
| Subject |
Estimation theory.
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Causation.
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Causality (Physics)
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MATHEMATICS -- Probability & Statistics -- General.
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Causality (Physics)
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Causation
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Estimation theory
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| Form |
Electronic book
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| Author |
Dawid, Philip.
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Bernardinelli, Luisa.
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| LC no. |
2012001517 |
| ISBN |
9781119941736 |
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1119941733 |
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9781119945703 |
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1119945704 |
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9781119941743 |
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1119941741 |
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9781119945710 |
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1119945712 |
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0470665564 |
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9780470665565 |
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