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Causal inference for statistics, social, and biomedical sciences : an introduction

Author: Guido W Imbens; Donald B Rubin
Publisher: New York : Cambridge University Press, 2015.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:

This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

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Genre/Form: Lehrbuch
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Guido W Imbens; Donald B Rubin
ISBN: 9780521885881 0521885884
OCLC Number: 910377690
Awards: Winner of PROSE Award for Textbook, Social Sciences 2016
Description: 625 S : Ill
Contents: Part I. Introduction: 1. The basic framework: potential outcomes, stability, and the assignment mechanism; 2. A brief history of the potential-outcome approach to causal inference; 3. A taxonomy of assignment mechanisms; Part II. Classical Randomized Experiments: 4. A taxonomy of classical randomized experiments; 5. Fisher's exact P-values for completely randomized experiments; 6. Neyman's repeated sampling approach to completely randomized experiments; 7. Regression methods for completely randomized experiments; 8. Model-based inference in completely randomized experiments; 9. Stratified randomized experiments; 10. Paired randomized experiments; 11. Case study: an experimental evaluation of a labor-market program; Part III. Regular Assignment Mechanisms: Design: 12. Unconfounded treatment assignment; 13. Estimating the propensity score; 14. Assessing overlap in covariate distributions; 15. Design in observational studies: matching to ensure balance in covariate distributions; 16. Design in observational studies: trimming to ensure balance in covariate distributions; Part IV. Regular Assignment Mechanisms: Analysis: 17. Subclassification on the propensity score; 18. Matching estimators (Card-Krueger data); 19. Estimating the variance of estimators under unconfoundedness; 20. Alternative estimands; Part V. Regular Assignment Mechanisms: Supplementary Analyses: 21. Assessing the unconfoundedness assumption; 22. Sensitivity analysis and bounds; Part VI. Regular Assignment Mechanisms with Noncompliance: Analysis: 23. Instrumental-variables analysis of randomized experiments with one-sided noncompliance; 24. Instrumental-variables analysis of randomized experiments with two-sided noncompliance; 25. Model-based analyses with instrumental variables; Part VII. Conclusion: 26. Conclusions and extensions.
Responsibility: Guido W. Imbens, Donald B. Rubin.

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'This book offers a definitive treatment of causality using the potential outcomes approach. Both theoreticians and applied researchers will find this an indispensable volume for guidance and Read more...

 
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