"Edited and written by many prominent researchers in the field, the book covers both classical and modern topics. Each chapter is self-contained, making it a great reference book. The book is organized in a way that related topics are clustered together, enabling readers to easily navigate and read chapter by chapter. Overall, I enjoyed reading this book very much. [ ...] The book contains numerous real-data examples that aid readers in understanding the concepts and methods. Additionally, many chapters discuss the computational implementation of the corresponding methods. I am confident that researchers and practitioners will find this book to be an excellent resource for adjustment methods." -Raymond K.W. Wong in Journal of the American Statistical Association, December 2023
"The book benefits from a comprehensive collection of recent causal inference methods, offering a wide range of perspectives on weighting and matching techniques. While all the methods share the common goal of unbiased causal effect estimation in observational studies, each chapter clearly demonstrates its focus (eg, balancing covariates or using survival outcomes). In particular, each chapter includes data application examples at the end or incorporates application studies throughout. [ ...] I am grateful that this book contributes to expanding the accessibility of modern causal inference tools, bringing them together in a cohesive manner for researchers and educators who wish to learn, teach, and apply these methods to obtain unbiased causal evidence from potentially messy and unkindobservational studies." -Youjin Lee in Biometrics, September 2024
