Edition 
1st ed 
Descript 
1 online resource (415 pages) 

text txt rdacontent 

computer c rdamedia 

online resource cr rdacarrier 
Series 
Wiley Series in Probability and Statistics Ser 

Wiley Series in Probability and Statistics Ser

Note 
Intro  Causality: Statistical Perspectives and Applications  List of contributors  An overview of statistical causality  1 Statistical causality: Some historical remarks  1.1 Introduction  1.2 Key issues  1.3 Rothamsted view  1.4 An earlier controversy and its implications  1.5 Three versions of causality  1.6 Conclusion  References  2 The language of potential outcomes  2.1 Introduction  2.2 Definition of causal effects through potential outcomes  2.2.1 Subjectspecific 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  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 semiMarkovian models  3.2.4 Interventions in semiMarkovian 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 The decisiontheoretic approach to causal inference  4.1 Introduction  4.2 Decision theory and causality  4.2.1 A 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 Backdoor 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  5.1 Introduction 

5.2 A brief commentary on developments since 1970  5.2.1 Potential outcomes and missing data  5.2.2 The 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 Graphbased criteria of identifiability of causal questions  6.1 Introduction  6.2 Interventions from observations  6.3 The backdoor criterion, conditional ignorability, and covariate adjustment  6.4 The frontdoor criterion  6.5 Docalculus  6.6 General identification  6.7 Dormant independences and posttruncation constraints  References  7 Causal inference from observational data: A Bayesian predictive approach  7.1 Background  7.2 A 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  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  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 Crossclassifications by joint potential outcomes  10.1 Introduction  10.2 Bounds for the causal treatment effect in randomized trials with imperfect compliance  10.3 Identifying the complier 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  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 Gcomputation  11.3.2 Inverse probability of treatment weighting  11.3.3 Gestimation for additive and multiplicative models  11.3.4 Gestimation for logistic models  11.3.5 Casecontrol studies  11.3.6 Gestimation for additive hazard models  11.4 Estimation of natural direct and indirect effects  11.5 Discussion  Acknowledgements  References  12 The mediation formula: A guide to the assessment of causal pathways in nonlinear models  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 The 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 principalstrata direct effect  12.4 Conclusions  Acknowledgments  References  13 The sufficient cause framework in statistics, philosophy and the biomedical and social sciences  13.1 Introduction  13.2 The sufficient cause framework in philosophy  13.3 The sufficient cause framework in epidemiology and biomedicine  13.4 The sufficient cause framework in statistics  13.5 The 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  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  15.1 Introduction  15.2 Background  15.3 The scientific hypothesis  15.4 Data  15.5 A simple preliminary analysis  15.6 Testing for qualitative interaction  15.7 Discussion  Acknowledgments  References  16 Supplementary variables for causal estimation  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.B.1 Estimator given R, C and T recorded  16.B.2 Estimator given R, L and T recorded  16.C Expressions with correlation coefficients  16.C.1 Estimator given R, C and T recorded  16.C.2 Estimator given R, L and T recorded  16.C.3 Estimator given R, L, C and T recorded  16.D Derivation of I's  16.E Relation between ρ2rl and ρ2rl  References  17 Timevarying confounding: Some practical considerations in a likelihood framework  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 Timetoevent outcomes  17.2.6 Causal estimands  17.3 Identifying assumptions  17.4 Gcomputation formula  17.4.1 The formula  17.4.2 Plugin regression estimation  17.5 Implementation by Monte Carlo simulation  17.5.1 Simulating an endofstudy outcome  17.5.2 Simulating a timetoevent outcome  17.5.3 Inference  17.5.4 Losses to followup  17.5.5 Software  17.6 Analyses of simulated data  17.6.1 The 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  18.1 Introduction  18.2 Noncausal interpretations of an association  18.3 Dealing with confounders  18.4 'Natural experiments'  18.4.1 Genetically sensitive designs  18.4.2 Children of twins (CoT) design  18.4.3 Strategies to identify the key environmental risk feature  18.4.4 Designs for dealing with selection bias 

18.4.5 Instrumental variables to rule out reverse causation 

A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wideranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book 

Description based on publisher supplied metadata and other sources 

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries 
Link 
Print version: Berzuini, Carlo Causality : Statistical Perspectives and Applications
New York : John Wiley & Sons, Incorporated,c2012 9780470665565

Subject 
Estimation theory.;Causation.;Causality (Physics)


Electronic books

Alt Author 
Dawid, Philip


Bernardinell, Luisa

