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.

Dr. Haiyan Bai is a Professor at the University of Central Florida. She earned her Ph.D. in quantitative research methodology at the University of Cincinnati. Her research interests include issues that revolve around statistical/quantitative methods, specifically, propensity score methods, resampling techniques, research design, measurement, and the application of statistical methods in social and behavioral sciences.

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