Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Name: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives
Availability: OutOfStock
ISBN: 9780470090442

ISBN-10: 0470090448

ISBN-13: 9780470090442

Edition: 2005

Authors: Donald B. Rubin, Andrew Gelman, Xiao-Li Meng

List price: $135.00

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Description:

This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple…

Book details

List price: $135.00
Copyright year: 2005
Publisher: John Wiley & Sons, Limited
Publication date: 10/22/2004
Binding: E-Book
Pages: 436
Size: 5.98" wide x 9.02" long x 1.18" tall
Weight: 1.694
Language: English



Preface



Casual inference and observational studies



An overview of methods for causal inference from observational studies




Introduction



Approaches based on causal models



Canonical inference



Methodologic modeling



Conclusion



Matching in observational studies




The role of matching in observational studies



Why match?



Two key issues: balance and structure



Additional issues



Estimating causal effects in nonexperimental studies




Introduction



Identifying and estimating the average treatment effect



The NSWdata



Propensity score estimates



Conclusions



Medication cost sharing and drug spending in Medicare




Methods



Results



Study limitations



Conclusions and policy implications



A comparison of experimental and observational data analyses




Experimental sample



Constructed observational study



Concluding remarks



Fixing broken experiments using the propensity score




Introduction



The lottery data



Estimating the propensity scores



Results



Concluding remarks



The propensity score with continuous treatments




Introduction



The basic framework



Bias removal using the GPS



Estimation and inference



Application: the Imbens-Rubin-Sacerdote lottery sample



Conclusion



Causal inference with instrumental variables




Introduction



Key assumptions for the LATE interpretation of the IV estimand



Estimating causal effects with IV



Some recent applications



Discussion



Principal stratification




Introduction: partially controlled studies



Examples of partially controlled studies



Principal stratification



Estimands



Assumptions



Designs and polydesigns



Missing data modeling



Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues




Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies



Constraints



Complex estimand structures, inferential goals, and utility functions



Robustness



Closing remarks



Bridging across changes in classification systems




Introduction



Multiple imputation to achieve comparability of industry and occupation codes



Bridging the transition from single-race reporting to multiple-race reporting



Conclusion



Representing the Census undercount by multiple imputation of households




Introduction



Models



Inference



Simulation evaluations



Conclusion



Statistical disclosure techniques based on multiple imputation




Introduction



Full synthesis



SMIKe and MIKe



Analysis of synthetic samples



An application



Conclusions



Designs producing balanced missing data: examples from the National Assessment of Educational Progress




Introduction



Statistical methods in NAEP



Split and balanced designs for estimating population parameters



Maximum likelihood estimation



The role of secondary covariates



Conclusions



Propensity score estimation with missing data




Introduction



Notation



Applied example:March of Dimes data



Conclusion and future directions



Sensitivity to nonignorability in frequentist inference




Missing data in clinical trials



Ignorability and bias



A nonignorable selection model



Sensitivity of the mean and variance



Sensitivity of the power



Sensitivity of the coverage probability



An example



Discussion



Statistical modeling and computation



Statistical modeling