Donald B. Rubin

Matched Sampling for Causal Effects

• Gebundenes Buch

Produktbeschreibung

This selection of Donald B. Rubin's contributions to matched sampling includes classic theoretical studies and real applications.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 502
• Erscheinungstermin: 30. Juni 2010
• Englisch
• Abmessung: 240mm x 161mm x 31mm
• Gewicht: 915g
• ISBN-13: 9780521857628
• ISBN-10: 0521857627
• Artikelnr.: 22737090

Autorenporträt

Professor Donald B. Rubin is the John L. Loeb Professor of Statistics in the Department of Statistics at Harvard University. Professor Rubin is a fellow of the American Statistical Association, the Institute for Mathematical Statistics, the International Statistical Institute, the Woodrow Wilson Society, the John Simon Guggenheim Society, the New York Academy of Sciences, the American Association for the Advancement of Sciences, and the American Academy of Arts and Sciences. He is also the recipient of the Samuel S. Wilks Medal of the American Statistical Association, the Parzen Prize for Statistical Innovation, and the Fisher Lectureship. Professor Rubin has lectured extensively throughout the United States, Europe, and Asia. He has over 300 publications (including several books) on a variety of statistical topics and is one of the top ten highly cited writers in mathematics in the world, according to ISI Science Watch.

Inhaltsangabe

Part I. The Early Years and the Influence of William G. Cochran: 1. William
G. Cochran's contributions to the design, analysis, and evaluation of
observational studies; 2. Controlling bias in observational studies: a
review William G. Cochran; Part II. Univariate Matching Methods and the
Dangers of Regression Adjustment: 3. Matching to remove bias in
observational studies; 4. The use of matched sampling and regression
adjustment to remove bias in observational studies; 5. Assignment to
treatment group on the basis of a covariate; Part III. Basic Theory of
Multivariate Matching: 6. Multivariate matching methods that are equal
percent bias reducing, I: Some examples; 7. Multivariate matching methods
that are equal percent bias reducing, II: Maximums on bias reduction for
fixed sample sizes; 8. Using multivariate matched sampling and regression
adjustment to control bias in observational studies; 9. Bias reduction
using Mahalanobis-metric matching; Part IV. Fundamentals of Propensity
Score Matching: 10. The central role of the propensity score in
observational studies for causal effects Paul R. Rosenbaum; 11. Assessing
sensitivity to an unobserved binary covariate in an observational study
with binary outcome Paul R. Rosenbaum; 12. Reducing bias in observational
studies using subclassification on the propensity score Paul R. Rosenbaum;
13. Constructing a control group using multivariate matched sampling
methods that incorporate the propensity score Paul Rosenbaum; 14. The bias
due to incomplete matching Paul R. Rosenbaum; Part V: Affinely Invariant
Matching Methods with Ellipsoidally Symmetric Distributions, Theory and
Methodology: 15. Affinely invariant matching methods with ellipsoidal
distributions Neal Thomas; 16. Characterizing the effect of matching using
linear propensity score methods with normal distributions Neal Thomas; 17.
Matching using estimated propensity scores: relating theory to practice
Neal Thomas; 18. Combining propensity score matching with additional
adjustments for prognostic covariates; Part VI. Some Applied Contributions:
19. Causal inference in retrospective studies Paul Holland; 20. The design
of the New York school choice scholarships program evaluation Jennifer Hill
and Neal Thomas; 21. Estimating and using propensity scores with partially
missing data Ralph D'Agostino Jr.; 22. Using propensity scores to help
design observational studies: application to the tobacco litigation; Part
VII. Some Focused Applications: 23. Criminality, aggression and
intelligence in XYY and XXY men H. A. Witkin; 24. Practical implications of
modes of statistical inference for causal effects and the critical role of
the assignment mechanism; 25. In utero exposure to phenobarbital and
intelligence deficits in adult men June Reinisch, Stephanie Sanders, and
Erik Mortensen; 26. Estimating causal effects from large data sets using
propensity scores; 27. On estimating the causal effects of DNR orders
Martin McIntosh.