Practical Propensity Score Methods Using R 1st Edition by Walter Leite ISBN 1452288887 9781452288888

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ISBN 10: 1452288887
ISBN 13:  978-1452288888
Author: Walter L. Leite
Practical Propensity Score Methods Using R 1st Edition: Practical Propensity Score Methods Using R by Walter Leite is a practical book that uses a step-by-step analysis of realistic examples to help students understand the theory and code for implementing propensity score analysis with the R statistical language. With a comparison of both well-established and cutting-edge propensity score methods, the text highlights where solid guidelines exist to support best practices and where there is scarcity of research. Readers will find that this scaffolded approach to R and the book’s free online resources help them apply the text’s concepts to the analysis of their own data.

 

Practical Propensity Score Methods Using R 1st Edition Table of contents:

Chapter 1 | Overview of Propensity Score Analysis

1.1 Introduction
1.2 Rubin’s Causal Model
1.3 Campbell’s Framework
1.4 Propensity Scores
1.5 Description of Example
1.6 Steps of Propensity Score Analysis
1.7 Propensity Score Analysis With Complex Survey Data
1.8 Resources for Learning R
1.9 Conclusion

Chapter 2 | Propensity Score Estimation

2.1 Introduction
2.2 Description of Example
2.3 Selection of Covariates
2.4 Dealing With Missing Data
2.5 Methods for Propensity Score Estimation
2.6 Evaluation of Common Support
2.7 Conclusion

Chapter 3 | Propensity Score Weighting

3.1 Introduction
3.2 Description of Example
3.3 Calculation of Weights
3.4 Covariate Balance Check
3.5 Estimation of Treatment Effects With Propensity Score Weighting
3.6 Propensity Score Weighting With Multiple Imputed Data Sets
3.7 Doubly Robust Estimation of Treatment Effect With Propensity Score Weighting
3.8 Sensitivity Analysis
3.9 Conclusion

Chapter 4 | Propensity Score Stratification

4.1 Introduction
4.2 Description of Example
4.3 Propensity Score Estimation
4.4 Propensity Score Stratification
4.5 Marginal Mean Weighting Through Stratification
4.6 Conclusion

Chapter 5 | Propensity Score Matching

5.1 Introduction
5.2 Description of Example
5.3 Propensity Score Estimation
5.4 Propensity Score Matching Algorithms
5.5 Evaluation of Covariate Balance
5.6 Estimation of Treatment Effects
5.7 Sensitivity Analysis
5.8 Conclusion

Chapter 6 | Propensity Score Methods for Multiple Treatments

6.1 Introduction
6.2 Description of Example
6.3 Estimation of Generalized Propensity Scores With Multinomial Logistic Regression
6.4 Estimation of Generalized Propensity Scores With Data Mining Methods
6.5 Propensity Score Weighting for Multiple Treatments
6.6 Estimation of Treatment Effect of Multiple Treatments
6.7 Conclusion

Chapter 7 | Propensity Score Methods for Continuous Treatment Doses

7.1 Introduction
7.2 Description of Example
7.3 Generalized Propensity Scores
7.4 Inverse Probability Weighting
7.5 Conclusion

Chapter 8 | Propensity Score Analysis With Structural Equation Models

8.1 Introduction
8.2 Description of Example
8.3 Latent Confounding Variables
8.4 Estimation of Propensity Scores
8.5 Propensity Score Methods
8.6 Treatment Effect Estimation With Multiple-Group Structural Equation Models
8.7 Treatment Effect Estimation With Multiple-Indicator and Multiple-Causes Models
8.8 Conclusion

Chapter 9 | Weighting Methods for Time-Varying Treatments

9.1 Introduction
9.2 Description of Example
9.3 Inverse Probability of Treatment Weights
9.4 Stabilized Inverse Probability of Treatment Weights
9.5 Evaluation of Covariate Balance
9.6 Estimation of Treatment Effects
9.7 Conclusion

Chapter 10 | Propensity Score Methods With Multilevel Data

10.1 Introduction
10.2 Description of Example
10.3 Estimation of Propensity Scores With Multilevel Data
10.4 Propensity Score Weighting
10.5 Treatment Effect Estimation
10.6 Conclusion

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