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書籍詳細




洋書

統計学・社会科学・生医学のための因果推論入門

Causal Inference for Statistics, Social, and Biomedical Sciences : An Introduction

Imbens, Guido W.   Rubin, Donald B.

Cambridge Univ Pr 2015/05
625 p. 18 b/w illus. 26 cm   
装丁: Hrd    装丁について
テキストの言語: ENG    出版国: GB
ISBN: 9780521885881
KCN: 1020803740
紀伊國屋書店 選定タイトル
標準価格:¥9,402(本体 ¥8,548)   
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納期について
DDC: 519.54
KDC: F16 確率・統計
C11 社会調査法・統計学
G405 医療統計学・情報学
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Annotation

In this groundbreaking text, two world-renowned experts lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher. Winner, 2016 PROSE Award for Textbook, Social Sciences.

Full Description

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

Table of 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.