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Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
Edizione [1st ed.]
Pubbl/distr/stampa Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (437 p.)
Disciplina 519.5/42
519.542
Altri autori (Persone) RubinDonald B
GelmanAndrew
MengXiao-Li
Collana Wiley series in probability and statistics
Soggetto topico Bayesian statistical decision theory
Missing observations (Statistics)
ISBN 1-280-26898-0
9786610268986
0-470-09045-6
0-470-09044-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks
7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification
9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes
11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction
14.2 Statistical methods in NAEP
Record Nr. UNINA-9910144711403321
Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
Edizione [1st ed.]
Pubbl/distr/stampa Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (437 p.)
Disciplina 519.5/42
519.542
Altri autori (Persone) RubinDonald B
GelmanAndrew
MengXiao-Li
Collana Wiley series in probability and statistics
Soggetto topico Bayesian statistical decision theory
Missing observations (Statistics)
ISBN 1-280-26898-0
9786610268986
0-470-09045-6
0-470-09044-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks
7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification
9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes
11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction
14.2 Statistical methods in NAEP
Record Nr. UNINA-9910829865903321
Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
Edizione [1st ed.]
Pubbl/distr/stampa Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (437 p.)
Disciplina 519.5/42
Altri autori (Persone) RubinDonald B
GelmanAndrew
MengXiao-Li
Collana Wiley series in probability and statistics
Soggetto topico Bayesian statistical decision theory
Missing observations (Statistics)
ISBN 1-280-26898-0
9786610268986
0-470-09045-6
0-470-09044-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks
7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification
9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes
11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction
14.2 Statistical methods in NAEP
Record Nr. UNINA-9910876584003321
Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui