Propensity Score Analysis

Statistical Methods and Applications
Second Edition
Shenyang Guo
Mark W. Fraser
Advanced Quantitative Techniques in the Social Sciences
Propensity Score Analysis
July 2014 | 448 pages | Sage US
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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.



Contents

List of Tables

List of Tables

List of Figures

List of Figures

Preface

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

References

Index

Index

Additional materials

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.



Contents

List of Tables

List of Tables

List of Figures

List of Figures

Preface

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

References

Index

Index

Additional materials

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Propensity Score Analysis

Statistical Methods and Applications

Shenyang Guo , Mark W. Fraser
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July 2014 | 448 pages | Sage US

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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

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