Practical propensity score methods using R /

Practical propensity score methods using R /

Show other versions (3)

This practical book 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...

Full description

Bibliographic Details
Main Author: Leite, Walter
Format: Book
Language:English
Published: Los Angeles, California : Sage, [2017]
Subjects:
Quantitative research
R (Computer program language)
Social sciences > Research > Statistical methods
Analyse multivariée
Statistique
R (logiciel)
Table of Contents:
  • Machine generated contents note: ch. 1 Overview of Propensity Score Analysis
  • Learning Objectives
  • 1.1 Introduction
  • 1.2. Rubin's Causal Model
  • 1.2.1. Potential Outcomes
  • 1.2.2. Types of Treatment Effects
  • 7.2.3. Assumptions
  • 1.3. Campbell's Framework
  • 1.4. Propensity Scores
  • 1.5. Description of Example
  • 1.6. Steps of Propensity Score Analysis
  • 1.6.1. Data Preparation
  • 1.6.2. Propensity Score Estimation
  • 1.6.3. Propensity Score Method Implementation
  • 1.6.4. Covariate Balance Evaluation
  • 1.6.5. Treatment Effect Estimation
  • 1.6.6. Sensitivity Analysis
  • 1.7. Propensity Score Analysis With Complex Survey Data
  • 1.8. Resources for Learning R
  • 1.8.1. R Packages for Propensity Score Analysis
  • 1.9. Conclusion
  • Study Questions
  • ch. 2 Propensity Score Estimation
  • Learning Objectives
  • 2.1. Introduction
  • 2.2. Description of Example
  • 2.3.
  • Conclusion
  • Study Questions
  • ch. 8 Propensity Score Analysis With Structural Equation Models
  • Learning Objectives
  • 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
  • Study Questions
  • ch. 9 Weighting Methods for Time-Varying Treatments
  • Learning Objectives
  • 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
  • 9.6.1. Weighted Regression With Cluster-Robust Standard Errors
  • 9.6.2.
  • Generalized Estimating Equations
  • 9.7 Conclusion
  • Study Questions
  • ch. 10 Propensity Score Methods With Multilevel Data
  • Learning Objectives
  • 10.1. Introduction
  • 10.2. Description of Example
  • 10.3. Estimation of Propensity Scores With Multilevel Data
  • 10.3.1. Multilevel Logistic Regression
  • 10.3.2. Logistic Regression With Fixed Cluster Effects
  • 10.4. Propensity Score Weighting
  • 10.5. Treatment Effect Estimation
  • 10.6. Conclusion
  • Study Questions
  • References.
  • 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
  • 6.5.1. Covariate Balance With Weights From Multinomial Logistic Regression
  • 6.5.2. Covariate Balance With Weights From Generalized Boosted Modeling
  • 6.5.3. Marginal Mean Weighting Through Stratification for Multiple Treatment Versions
  • 6.6. Estimation of Treatment Effect of Multiple Treatments
  • 6.7. Conclusion
  • Study Questions
  • ch. 7 Propensity Score Methods for Continuous Treatment Doses
  • Learning Objectives
  • 7.1. Introduction
  • 7.2. Description of Example
  • 7.3. Generalized Propensity Scores
  • 7.3.7. Dose Response Function
  • 7.4. Inverse Probability Weighting
  • 7.4.1. Estimation of the Average Treatment Effect
  • 7.5.
  • Propensity Score Estimation
  • 4.4 Propensity Score Stratification
  • 4.4.7. Covariate Balance Evaluation
  • 4.4.2. Estimation of Treatment Effects
  • 4.5. Marginal Mean Weighting Through Stratification
  • 4.5.7. Covariate Balance Evaluation
  • 4.5.2. Estimation of Treatment Effect
  • 4.5.3. Doubly Robust Estimation With MMWS
  • 4.6. Conclusion
  • Study Questions
  • ch. 5 Propensity Score Matching
  • Learning Objectives
  • 5.1. Introduction
  • 5.2. Description of Example
  • 5.3. Propensity Score Estimation
  • 5.4. Propensity Score Matching Algorithms
  • 5.4.7. Greedy Matching
  • 5.4.2. Genetic Matching
  • 5.4.3. Optimal Matching
  • 5.4.4. Full Matching
  • 5.5. Evaluation of Covariate Balance
  • 5.6. Estimation of Treatment Effects
  • 5.7. Sensitivity Analysis
  • 5.8. Conclusion
  • Study Questions
  • ch. 6 Propensity Score Methods for Multiple Treatments
  • Learning Objectives
  • 6.1.
  • Selection of Covariates
  • 2.4 Dealing With Missing Data
  • 2.5. Methods for Propensity Score Estimation
  • 2.5.7. Logistic Regression
  • 2.5.2. Recursive Partitioning Algorithms
  • 2.5.3. Generalized Boosted Modeling
  • 2.6. Evaluation of Common Support
  • 2.7. Conclusion
  • Study Questions
  • ch. 3 Propensity Score Weighting
  • Learning Objectives
  • 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
  • Study Questions
  • ch. 4 Propensity Score Stratification
  • Learning Objectives
  • 4.1. Introduction
  • 4.2. Description of Example
  • 4.3.

Similar Items