新书公告：Propensity Score Analysis: Statistical Methods and Applications,2nd ed

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收藏 2014-08-03

Propensity Score Analysis: Statistical Methods and Applications, Second Edition
英文原书第一版和基于第一版的中文翻译版论坛内都有坛友上传了。

Authors:	Shenyang Y. Guo and Mark W. Fraser
Publisher:	Sage
Copyright:	2014
ISBN-13:	978-1-4522-3500-4
Pages:	448; hardcover
Price:	$69.50

封面：

＝＝＝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.

The most significant change of the second edition is discussion of propensity score subclassification, propensity score weighting, and dosage analysis from Chapter 5 to separate chapters. These methods are closely related to the Rosenbaum and Rubin’s (1983) seminal study of the development of propensity scores—it is for this reason that Chapter 5 of the first edition pooled these methods together. Because subclassification and weighting methods have been widely applied in recent research and have become recommended models for addressing challenging data issues (Imbens & Wooldridge, 2009), we decided to give each topic a separate treatment. There is an increasing need in social behavioral and health research to model treatment dosage and to extend the propensity score approach from the binary treatment conditions context to categorical and/or continuous treatment conditions contexts. Given these considerations, we treated dosage analysis in the second edition as a separate chapter. As a result, Chapter 5 now focuses on propensity score matching methods alone, including greedy matching and optimal matching.

＝＝＝Features/New to This Edition＝＝＝
NEW TO THIS EDITION:

Propensity score sub-classification and propensity score weighting are treated as separate models to give thorough attention to each.
Newly expanded coverage of analyzing treatment dosage in the context of propensity score modeling broadens the scope of application for readers.
New coverage of modeling heterogeneous treatment effects includes two nonparametric tests and a discussion of modeling issues to ensure students are on the cutting edge.
Expanded content on propensity score analysis with multilevel data includes new discussions of four multilevel models for estimating propensity scores and two strategies for controlling clustering effects in outcome analysis.
The principles and issues related to running propensity score models with sub-classification and weighting are covered in depth.
The authors demonstrate new software and include clear illustrations for analyzing treatment dosage with GPS.

KEY FEATURES:

The authors present key information on model derivations and summarize complex statistical arguments—omitting their proofs to challenge readers to apply their learning.
Each method, and its empirical examples, is linked to specific Stata programs for seamless integration of learning and application.
Two conceptual frameworks—the Neyman-Rubin counterfactual framework and the Heckman econometric model of causality—provide a foundation for understanding key topics.
Examples in every chapter demonstrate real challenges found in social and health sciences research.
Data simulation is used to illustrate key points.
New statistical approaches necessary for understanding the seven evaluation methods are included.

＝＝＝Sample Materials and Chapters＝＝＝
Chapter 1
Chapter 2




资料来源：
http://www.sagepub.com/books/Book238151/toc#tabview=toc

http://www.stata.com/bookstore/propensity-score-analysis/

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hiderm

2014-8-3 13:20:26

Table of contents

View table of contents >>

List of Tables
List of Figures
Preface
      1. What the Book Is About
      2. New in the Second Edition
      3. Acknowledgments
About the Authors

      1. Introduction
            1.1 Observational Studies
            1.2 History and Development
            1.3 Randomized Experiments
                     1.3.1 Fisher's Randomized Experiment
                     1.3.2 Types of Randomized Experiments and Statistical Tests
                     1.3.3 Critiques of Social Experimentation
            1.4 Why and When a Propensity Score Analysis Is Needed
            1.5 Computing Software Packages
            1.6 Plan of the Book

      2. Counterfactual Framework and Assumptions
            2.1 Causality, Internal Validity, and Threats
            2.2 Counterfactuals and the Neyman-Rubin Counterfactual Framework
            2.3 The Ignorable Treatment Assignment Assumption
            2.4 The Stable Unit Treatment Value Assumption
            2.5 Methods for Estimating Treatment Effects
                     2.5.1 Design of Observational Study
                     2.5.2 The Seven Models
                     2.5.3 Other Balancing Methods
                     2.5.4 Instrumental Variables Estimator
                     2.5.5 Regression Discontinuity Designs
            2.6 The Underlying Logic of Statistical Inference
            2.7 Types of Treatment Effects
            2.8 Treatment Effect Heterogeneity
                     2.8.1 The Importance of Studying Treatment Effect Heterogeneity
                     2.8.2 Checking the Plausability of the Unconfoundedness Assumption
                     2.8.3 A Methodological Note About the Hausman Test of Endogeneity
                     2.8.4 Tests of Treatment Effect Heterogeneity
                     2.8.5 Example
            2.9 Heckman's Econometric Model of Causality
            2.10 Conclusion

      3. Conventional Methods for Data Balancing
            3.1 Why Is Data Balancing Necessary? A Heuristic Example
            3.2 Three Methods for Data Balancing
                     3.2.1 The Ordinary Least Squares Regression
                     3.2.2 Matching
                     3.2.3 Stratification
            3.3 Design of the Data Simulation
            3.4 Results of the Data Simulation
            3.5 Implications of the Data Simulation
            3.6 Key Issues Regarding the Application of OLS Regression
            3.7 Conclusion

      4. Sample Selection and Related Models
            4.1 The Sample Selection Model
                     4.1.1 Truncation, Censoring, and Incidental Truncation
                     4.1.2 Why Is It Important to Model Sample Selection?
                     4.1.3 Moments of an Incidentally Truncated Bivariate Normal Distribution
                     4.1.4 The Heckman Model and Its Two-Step Estimator
            4.2 Treatment Effect Model
               4.3 Overview of the Stata Programs and Main Features of treatreg
            4.4 Examples
                     4.4.1 Application of the Treatment Effect model to Analysis of Observational Data
                     4.4.2 Evaluation of Treatment Effects from a Program With a Group Randomization Design
                     4.4.3 Running the Treatment Effect Model After Multiple Imputations of Missing Data
            4.5 Conclusion

      5. Propensity Score Matching and Related Models
            5.1 Overview
            5.2 The Problem of Dimensionality and the Properties of Propensity Scores
            5.3 Estimating Propensity Scores
                     5.3.1 Binary Logistic Regression
                     5.3.2 Strategies to Specify a Correct Model—Predicting Propensity Scores
                     5.3.3 Hirano and Imbens's Method for Specifying Predictors Relying on Predetermined Critical t Values
                     5.3.4 Generalized Boosted Modeling
            5.4 Matching
                     5.4.1 Greedy Matching
                     5.4.2 Optimal Matching
                     5.4.3 Fine Balance
            5.5 Postmatching Analysis
                     5.5.1 Multivariate Analysis After Greedy Matching
                     5.5.2 Computing Indices of Covariate Imbalance
                     5.5.3 Outcome Analysis Using the Hodges-Lehmann Aligned Rank Test After Optimal Matching
                     5.5.4 Regression Adjustment Based on Sample Created by Optimal Pair Matching
                     5.5.5 Regression Adjustment Using Hodges-Lehmann Aligned Rank Scores After Optimal Matching
            5.6 Propensity Score Matching With Multilevel Data
                     5.6.1 Overview of Statistical Approaches to Multilevel Data
                     5.6.2 Perspectives Extending the Propensity Score Analysis to the Multilevel Modeling
                     5.6.3 Estimation of the Propensity Scores Under the Context of Multilevel Modeling
                     5.6.4 Multilevel Outcome Analysis
            5.7 Overview of the Stata and R Programs
            5.8 Examples
                     5.8.1 Greedy Matching and Subsequent Analysis of Hazard Rates
                     5.8.2 Optimal Matching
                     5.8.3 Post-Full Matching Analysis Using the Hodges-Lehmann Aligned Rank Test
                     5.8.4 Post-Pair Matching Analysis Using Regression of Difference Scores
                     5.8.5 Multilevel Propensity Score Analysis
                     5.8.6 Comparison of Rand-gbm and Stata's boost Algorithms
            5.9 Conclusion

      6. Propensity Score Subclassification
            6.1 Overview
            6.2 The Overlap Assumption and Methods to Address Its Violation
            6.3 Structural Equation Modeling With Propensity Score Subclassification
                     6.3.1 The Need for Integrating SEM and Propensity Score Modeling Into One Analysis
                     6.3.2 Kaplan's (1999) Work to Integrate Propensity Score Subclassification With SEM
                     6.3.3 Conduct SEM With Propensity Score Subclassification
            6.4 The Stratification-Multilevel Method
            6.5 Examples
                     6.5.1 Stratification After Greedy Matching
                     6.5.2 Subclassification Followed by a Cox Proportional Hazards Model
                     6.5.3 Propensity Score Subclassification in Conjunction with SEM
            6.6 Conclusion

      7. Propensity Score Weighting
            7.1 Overview
            7.2 Weighting Estimators
                     7.2.1 Formulas for Creating Weights to Estimate ATE and ATT
                     7.2.2 A Corrected Version of Weights Estimating ATE
                     7.2.3 Steps in Propensity Score Weighting
            7.3 Examples
                     7.3.1 Propensity Score Weighting With a Multiple Regression Outcome Analysis
                     7.3.2 Propensity Score Weighting With a Cox Proportional Hazards Model
                     7.3.3 Propensity Score Weighting With an SEM
                     7.3.4 Comparison of Models and Conclusions of the Study of the Impact of Poverty on Child Academic Achievement
            7.4 Conclusion

      8. Matching Estimators
            8.1 Overview
            8.2 Methods of Matching Estimators
                     8.2.1 Simple Matching Estimators
                     8.2.2 Bias-Corrected Matching Estimator
                     8.2.3 Variance Estimator Assuming Homoscedasticity
                     8.2.4 Variance Estimator Allowing for Heteroscedasticity
                     8.2.5 Large Sample Properties and Correction
            8.3 Overview of the Stata Program nnmatch
            8.4 Examples
                     8.4.1 Matching With Bias-Corrected and Robust Variance Estimators
                     8.4.2 Efficacy Subset Analysis With Matching Estimators
            8.5 Conclusion

      9. Propensity Score Analysis With Nonparametric Regression
            9.1 Overview
            9.2 Methods of Propensity Score Analysis With Nonparametric Regression
                     9.2.1 The Kernel-Based Matching Estimators
                     9.2.2 Review of the Basic Concepts of Local Linear Regression (lowess)
                     9.2.3 Asymptotic and Finite-Sample Properties of Kernel and Local Linear Matching
            9.3 Overview of the Stata Programs psmatch2 and bootstrap
            9.4 Examples
                     9.4.1 Analysis of Difference-in-Differences
                     9.4.2 Application of Kernel-Based Matching to One-Point Data
            9.5 Conclusion

      10. Propensity Score Analysis of Categorical or Continuous Treatments: Dosage Analyses
            10.1 Overview
            10.2 Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression
            10.3 Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model
            10.4 The Generalized Propensity Score Estimator
            10.5 Overview of the Stata gpscore Program
            10.6 Examples
                     10.6.1 Modeling Doses of Treatment With Multiple Balancing Scores Estimated by a Multinomial Logit Model
                     10.6.2 Modeling Doses of Treatment With the Generalized Propensity Score Estimator
            10.7 Conclusion

      11. Selection Bias and Sensitivity Analysis
            11.1 Selection Bias: An Overview
                     11.1.1 Sources of Selection Bias
                     11.1.2 Overt Bias Versus Hidden Bias
                     11.1.3 Consequences of Selection Bias
                     11.1.4 Strategies to Correct for Selection Bias
            11.2 A Monte Carlo Study Comparing Corrective Models
                     11.2.1 Design of the Monte Carlo Study
                     11.2.2 Results of the Monte Carlo Study
                     11.2.3 Implications
            11.3 Rosenbaum's Sensitivity Analysis
                     11.3.1 The Basic Idea
                     11.3.2 Illustration of Wilcoxon's Signed Rank Test for Sensitivity Analysis of a Matched Pair Study
            11.4 Overview of the Stata Program rbounds
            11.5 Examples
                     11.5.1 Sensitivity Analysis of the Effects of Lead Exposure
                     11.5.2 Sensitivity Analysis for the Study Using Pair Matching
            11.6 Conclusion

      12. Concluding Remarks
            12.1 Common Pitfalls in Observational Studies: A Checklist for Critical Review
            12.2 Approximating Experiments With Propensity Score Approaches
                     12.2.1 Criticism of Propensity Score Methods
                     12.2.2 Regression and Propensity Score Approaches: Do They Provide Similar Results?
                     12.2.3 Criticism of Sensitivity Analysis (Γ)
                     12.2.4 Group Randomized Trials
            12.3 Other Advances in Modeling Causality
            12.4 Directions for Future Development

References
Index