Propensity Score Methods and Applications: Quantitative Applications in the Social Sciences

“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.”
“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.”
“This book contains excellent descriptions of propensity score matching with practical examples and clear guides using different software programs.”

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

A concise, introductory text on propensity score methods that is easy to comprehend by those who have a limited background in statistics, and is practical enough for researchers to quickly generalize and apply the methods.