Book review of “Causal Inference for Statistics, Social, and Biomedical Sciences” (authors: G.W. Imbens and D.B. Rubin).
Extracting information and drawing inferences about causal effects of treatments, interventions and actions is central to decision making in many disciplines and is broadly viewed as causal inference. In this groundbreaking book, Guido Imbens and Don Rubin tell us what statistics can say about causation and present statistical methods for studying causal questions. The book focuses on the most widely used statistical framework for causal inference: the potential outcome framework, also known as the Rubin Causal Model (RCM), a term coined by Holland (1986).