Fundamentals of causal inference with R/ Babette A Brumback

Includes bibliography and index

Introduction -- Conditional probability and expectation -- Potential outcomes and the fundamental problem of causal inference -- Effect-measure modification and causal interaction -- Causal directed acyclic graphs -- Adjusting for confounding: backdoor method via standardization -- Adjusting for confounding: difference-in-differences estimators -- Adjusting for confounding: front-door method -- Adjusting for confounding instrumental variables -- Adjusting for confounding: propensity-score methods -- Gaining efficiency with precision variables -- Mediation -- Adjusting for time-dependent confounding

This book provides an excellent introduction to causal inference methods and their implementations using R. The book is well-written, with many technical concepts and methods explained using real examples. As the way the book is designed, it can make an excellent introductory textbook for undergraduate and graduate students in Statistics, Biostatistics, and related fields, and a useful resource for researchers seeking to use observational data to generate evidence of causality."