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Matched Sampling for Causal Effects

ISBN-10: 0521674360
ISBN-13: 9780521674362
Edition: 2006
Authors: Donald B. Rubin
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Description: Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles  More...

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Book details

Copyright year: 2006
Publisher: Cambridge University Press
Publication date: 9/4/2006
Binding: Paperback
Pages: 502
Size: 6.25" wide x 9.50" long x 1.00" tall
Weight: 1.496
Language: English

Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers in statistics, epidemiology, medicine, economics, education, sociology, political science, and anyone else doing empirical research to evaluate the causal effects of interventions.

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

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