Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Comments on Imbens and Rubin causal inference book
Imbens and Donald B. Rubin in Cambridge Books from Cambridge University Press Abstract: Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies.
Guido Imbens and Don Rubin recently came out with a book on causal inference. Imbens and Rubin come from social science and econometrics. Meanwhile, Miguel Hernan and Jamie Robins are finishing up their own book on causal inference, which has more of a biostatistics focus. Comments on table of contents and the 5 sample chapters of Causal Inference in Statistics, by Rubin and Imbens. First off, Rubin and Imbens are the leaders in the field of causal inference.