Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

摘要

A classic is born! The label classic tends to be overused and sometimes its significance as a moniker is devalued. This devaluation will not be the case with Imbens and Rubin's Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. In this reviewer's prognosis, this book will inspire and educate future generations of students and researchers dealing with serious research questions. Just as Paul Samuelson and William Nordhaus' Economics (2009) and Philip Kotler and Kevin Keller's Marketing Management (2011) have shaped their respective disciplines, Causal Inference is expected to formalize and standardize concepts, techniques and tools to analyze causality in many disciplines. Unlike the above mentioned text books that are geared towards undergraduates Unlike the textbooks just noted, which are geared towards undergraduates, this book is for graduate students and researchers in various fields, yet it retains the essential elements of classics-simplicity and clarity. In addition, completeness to the causality story can be expected as of the early 21st century when the promised sequel is published.