Practical Propensity Score Methods Using R
- Walter Leite - University of Florida, USA
Data Collection & Analysis | Quantitative Evaluation | Quantitative/Statistical Research (General)
Student Study Site
The open-access Student Study Site is an essential resource to complement the book. The site contains all the code presented in the book fully commented, datasets, and alternative implementations for some of the methods shown in the book.
“This book offers a comprehensive, accessible, and timely treatment of propensity score analysis and its application for estimating treatment effects from observational data with varying levels of complexity. Both novice and advanced users of this methodology will appreciate the breadth and depth of the practical knowledge that Walter Leite offers, and the useful examples he provides.”
“Clearly written and technically sound, this text should be a staple for researchers and methodologists alike. Not only is the text an excellent resource for understanding propensity score analysis, but the author has recognized the messiness of real data, and helps the reader understand and appropriately address issues such as missing data and complex samples. This is extremely refreshing.”
“This book provides an overview of propensity score analysis. The author’s introduction situates propensity score analysis within Rubin’s Causal Model and Campbell’s Framework. This text will be good for the advanced user with previous knowledge of the R language, complex survey design, and missing data.”
“This book provides an excellent definition of propensity scores and the sequential steps required in its application.”
“It is a well-crafted practical book on propensity score methods and features the free software R. I believe many students will like it.”
“With the use of examples consisting of real survey data, Practical Propensity Score Methods Using R provides a wide range of detailed information on how to reduce bias in research studies that seek to test treatment effects in situations where random assignment was not implemented.”
In general, the book is well-crafted and focuses on practical implementation of propensity score methods featuring the free software R. Even though there is room for improvement that could be addressed in a second edition, we believe that it is a useful book for researchers and graduate students, and therefore, many readers will find it beneficial.