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

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

19.02.2019

Verlag

Cambridge University Press

Seitenzahl

644

Maße (L/B/H)

26/18,3/3,9 cm

Gewicht

1280 g

Sprache

Englisch

ISBN

978-0-521-88588-1

Beschreibung

Rezension

'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 reference.' Hal Varian, Chief Economist, Google, and Emeritus Professor, University of California, Berkeley

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

19.02.2019

Verlag

Cambridge University Press

Seitenzahl

644

Maße (L/B/H)

26/18,3/3,9 cm

Gewicht

1280 g

Sprache

Englisch

ISBN

978-0-521-88588-1

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: Libri GmbH

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Die Leseprobe wird geladen.
  • Produktbild: Causal Inference for Statistics, Social, and Biomedical Sciences
  • 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.