Propensity Score Methods and Applications

Haiyan Bai - University of Central Florida, USA
M. H. Clark - University of Central Florida, USA
Quantitative Applications in the Social Sciences
Propensity Score Methods and Applications
November 2018 | 136 pages | Sage US
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Description

A concise, introductory text, Propensity Score Methods and Applications describes propensity score methods (PSM) and how they are used to balance the distributions of observed covariates between treatment conditions as a means to reduce selection bias. This new QASS title specifically focuses on the procedures of implementing PSM for research in social sciences, instead of merely demonstrating the effectiveness of the method.  

Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.

Contents

Series Editor’s Introduction

Series Editor’s Introduction

About the Authors

About the Authors

Acknowledgments

  • 1. Basic Concepts of Propensity Score Methods
  • 1.1 Causal Inference
  • 1.2 Propensity Scores
  • 1.3 Assumptions
  • 1.4 Summary of the Chapter
  • 2. Covariate Selection and Propensity Score Estimation
  • 2.1 Covariate Selection
  • 2.2 Propensity Score Estimation
  • 2.3 Summary of the Chapter
  • 2.4 An Example
  • 3. Propensity Score Adjustment Methods
  • 3.1 Propensity Score Matching
  • 3.2 Other Propensity Score Adjustment Methods
  • 3.3 Summary of the Chapter
  • 3.4 An Example
  • 4. Covariate Evaluation and Causal Effect Estimation
  • 4.1 Evaluating the Balance of Covariate Distributions
  • 4.2 Causal Effect Estimation
  • 4.3 Sensitivity Analysis
  • 4.4 Summary of the Chapter
  • 4.5 An Example
  • 5. Conclusion
  • 5.1 Limitations of the Propensity Score Methods and How to Address Them
  • 5.2 Summary of Propensity Score Procedures
  • 5.3 Final Comments

References

References

Index

Index

Description

A concise, introductory text, Propensity Score Methods and Applications describes propensity score methods (PSM) and how they are used to balance the distributions of observed covariates between treatment conditions as a means to reduce selection bias. This new QASS title specifically focuses on the procedures of implementing PSM for research in social sciences, instead of merely demonstrating the effectiveness of the method.  

Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.

Contents

Series Editor’s Introduction

Series Editor’s Introduction

About the Authors

About the Authors

Acknowledgments

  • 1. Basic Concepts of Propensity Score Methods
  • 1.1 Causal Inference
  • 1.2 Propensity Scores
  • 1.3 Assumptions
  • 1.4 Summary of the Chapter
  • 2. Covariate Selection and Propensity Score Estimation
  • 2.1 Covariate Selection
  • 2.2 Propensity Score Estimation
  • 2.3 Summary of the Chapter
  • 2.4 An Example
  • 3. Propensity Score Adjustment Methods
  • 3.1 Propensity Score Matching
  • 3.2 Other Propensity Score Adjustment Methods
  • 3.3 Summary of the Chapter
  • 3.4 An Example
  • 4. Covariate Evaluation and Causal Effect Estimation
  • 4.1 Evaluating the Balance of Covariate Distributions
  • 4.2 Causal Effect Estimation
  • 4.3 Sensitivity Analysis
  • 4.4 Summary of the Chapter
  • 4.5 An Example
  • 5. Conclusion
  • 5.1 Limitations of the Propensity Score Methods and How to Address Them
  • 5.2 Summary of Propensity Score Procedures
  • 5.3 Final Comments

References

References

Index

Index

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Propensity Score Methods and Applications

Haiyan Bai , M. H. Clark
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November 2018 | 136 pages | Sage US

Format Published Date ISBN Price

A concise, introductory text, Propensity Score Methods and Applications describes propensity score methods (PSM) and how they are used to balance the distributions of observed covariates between treatment conditions as a means to reduce selection bias. This new QASS title specifically focuses on the procedures of implementing PSM for research in social sciences, instead of merely demonstrating the effectiveness of the method.  

Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.


Table Of Contents:

  • Series Editor’s Introduction
  • About the Authors
  • Acknowledgments
  • 1. Basic Concepts of Propensity Score Methods
  • 1.1 Causal Inference
  • 1.2 Propensity Scores
  • 1.3 Assumptions
  • 1.4 Summary of the Chapter
  • 2. Covariate Selection and Propensity Score Estimation
  • 2.1 Covariate Selection
  • 2.2 Propensity Score Estimation
  • 2.3 Summary of the Chapter
  • 2.4 An Example
  • 3. Propensity Score Adjustment Methods
  • 3.1 Propensity Score Matching
  • 3.2 Other Propensity Score Adjustment Methods
  • 3.3 Summary of the Chapter
  • 3.4 An Example
  • 4. Covariate Evaluation and Causal Effect Estimation
  • 4.1 Evaluating the Balance of Covariate Distributions
  • 4.2 Causal Effect Estimation
  • 4.3 Sensitivity Analysis
  • 4.4 Summary of the Chapter
  • 4.5 An Example
  • 5. Conclusion
  • 5.1 Limitations of the Propensity Score Methods and How to Address Them
  • 5.2 Summary of Propensity Score Procedures
  • 5.3 Final Comments
  • References
  • Index

Recent Product Reviews:

“Haiyan Bai and M.H. Clark have delivered a readable and easily applicable guide for eager researchers with data-in-hand, chomping at the bit to determine whether and how their empirical challenges might be addressed through the careful application of propensity score methods.”
Adam Seth Litwin, Cornell University
“This volume provides a thorough introduction to propensity score methods while taking care to not overwhelm the reader with dense mathematics. Simple examples, straightforward language, and a catalog of software options make this a fine primer for researchers seeking to incorporate propensity score methods into their own research plans, and an excellent desk reference.”
Christopher Michael Sedelmaier, University of New Haven
“This book contains excellent descriptions of propensity score matching with practical examples and clear guides using different software programs.”
Mido Chang, Florida International University

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