Summary
Contents
Subject index
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 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.
Propensity Score Matching
Propensity Score Matching
Learning Objectives
- Describe and compare greedy, genetic, and optimal matching algorithms.
- Characterize the impact of matching with or without replacement on results and analysis choices.
- Compare one-to-one, fixed ratio, variable ratio, and full matching strategies.
- Implement methods to estimate treatment effects with samples obtained with different matching methods.
- Implement methods to estimate standard errors of treatment effects with samples obtained with different matching methods.
- Understand the rationale and implementation of Rosenbaum’s sensitivity analysis.
5.1. Introduction
This chapter presents the implementation of different propensity score matching methods, as well as a comparison of methods in terms of covariate balance and bias of treatment effect estimates. Propensity score matching consists of grouping observations with similar values of propensity scores. However, while propensity score weighting (see Chapter 3) and propensity score stratification ...
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