Matched Sampling for Causal Effects 1st Edition by Donald Rubin 9780521857628 0521857627

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ISBN 10: 0521857627 
ISBN 13: 9780521857628 
Author: Donald B. Rubin

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.

Matched Sampling for Causal Effects 1st Table of contents:

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

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
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
11. Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcomes
12. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
13. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
14. The Bias Due to Incomplete Matching

PART V: AFFINELY INVARIANT MATCHING METHODS WITH ELLIPSOIDALLY SYMMETRIC DISTRIBUTIONS, THEORY AND METHODS
15. Affinely Invariant Matching Methods with Ellipsoidal Distributions
16. Characterizing the Effect of Matching Using Linear Propensity Score Methods with Normal Distributions
17. Matching Using Estimated Propensity Scores: Relating Theory to Practice
18. Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates

PART VI: SOME APPLIED CONTRIBUTIONS
19. Causal Inference in Retrospective Studies

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