1 Preface

This book grew out of my course notes for a twelve-week course (one term) on the Design of Scientific Studies at the University of Toronto. I started writing my own notes because I wanted to expose undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected. I assume that the reader has taken basic courses in mathematical statistics, and linear models, although the essentials are reviewed briefly in the first chapter. Some experience using R is helpful although not essential.