電子書籍詳細

電子書籍詳細


洋書 kinoppy

傾向スコア分析(第2版)

Propensity Score Analysis : Statistical Methods and Applications

Second Edition

(Advanced Quantitative Techniques in the Social Sciences)

Guo, Shenyang   Fraser, Mark W.

SAGE Publications, Inc 2014/06
448p.
出版国: US
ISBN: 9781452235004
eISBN: 9781483322520
KNPID: EY00111193
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Full Description

Fully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application.

Table of Contents

List of Tables
List of Figures
Preface
About the Authors
Chapter 1: Introduction
Observational Studies
History and Development
Randomized Experiments
Why and When a Propensity Score Analysis Is Needed
Computing Software Packages
Plan of the Book
Chapter 2: Counterfactual Framework and Assumptions
Causality, Internal Validity, and Threats
Counterfactuals and the Neyman-Rubin Counterfactual Framework
The Ignorable Treatment Assignment Assumption
The Stable Unit Treatment Value Assumption
Methods for Estimating Treatment Effects
The Underlying Logic of Statistical Inference
Types of Treatment Effects
Treatment Effect Heterogeneity
Heckman’s Econometric Model of Causality
Conclusion
Chapter 3: Conventional Methods for Data Balancing
Why Is Data Balancing Necessary? A Heuristic Example
Three Methods for Data Balancing
Design of the Data Simulation
Results of the Data Simulation
Implications of the Data Simulation
Key Issues Regarding the Application of OLS Regression
Conclusion
Chapter 4: Sample Selection and Related Models
The Sample Selection Model
Treatment Effect Model
Overview of the Stata Programs and Main Features of treatreg
Examples
Conclusion
Chapter 5: Propensity Score Matching and Related Models
Overview
The Problem of Dimensionality and the Properties of Propensity Scores
Estimating Propensity Scores
Matching
Postmatching Analysis
Propensity Score Matching With Multilevel Data
Overview of the Stata and R Programs
Examples
Conclusion
Chapter 6: Propensity Score Subclassification
Overview
The Overlap Assumption and Methods to Address Its Violation
Structural Equation Modeling With Propensity Score Subclassification
The Stratification-Multilevel Method
Examples
Conclusion
Chapter 7: Propensity Score Weighting
Overview
Weighting Estimators
Examples
Conclusion
Chapter 8: Matching Estimators
Overview
Methods of Matching Estimators
Overview of the Stata Program nnmatch
Examples
Conclusion
Chapter 9: Propensity Score Analysis With Nonparametric Regression
Overview
Methods of Propensity Score Analysis With Nonparametric Regression
Overview of the Stata Programs psmatch2 and bootstrap
Examples
Conclusion
Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments
Overview
Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression
Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model
The Generalized Propensity Score Estimator
Overview of the Stata gpscore Program
Examples
Conclusion
Chapter 11: Selection Bias and Sensitivity Analysis
Selection Bias: An Overview
A Monte Carlo Study Comparing Corrective Models
Rosenbaum’s Sensitivity Analysis
Overview of the Stata Program rbounds
Examples
Conclusion
Chapter 12: Concluding Remarks
Common Pitfalls in Observational Studies: A Checklist for Critical Review
Approximating Experiments With Propensity Score Approaches
Other Advances in Modeling Causality
Directions for Future Development
References
Index