Design of experiments is, in essence, a disciplined way to learn about cause and effect. Modern experiments can involve a few to millions of units and hundreds or thousands of covariates. These settings demand tools that are flexible, transparent, and faithful to the underlying design in order to reach reliable conclusions about which interventions work and which ones do not. This book provides a modern, accessible, and computationally supported introduction to experimental design grounded firmly in randomization and the formulation of ideas and methods in terms of potential outcomes. Instead of prescribing a model for each design, we begin with the treatment assignment mechanism and link it directly to the observed outcomes through the potential outcomes framework. This formulation illuminates how changing the design changes the analysis, and it naturally distinguishes finite-population inference from super-population modeling. The book also incorporates new developments at the interface of causal inference and experimental design, many stemming from the authors' recent collaborative research efforts.
- Strengthens the link between design and analysis, enabling students to see immediately how the structure of an experiment shapes the exact tools used to analyze it.
- Teaches foundational concepts without assuming linear-model assumptions.
- Equips readers with the tools needed to analyze non-standard and complex experiments, whose randomization mechanisms fall outside the scope of traditional textbooks.
- Support students with limited programming experience by providing algorithms and code throughout the book, enabling them to implement randomization-based methods easily and efficiently.
This book is a textbook for one/two semester course on introductory experimental design.
Tirthankar Dasgupta is a Professor of Statistics at Rutgers University, New Jersey. Prior to joining Rutgers University, he served as a faculty member at Harvard University. His primary research interests include experimental design and causal inference. He is a fellow of the American Statistical Association. He has published about 50 peer-reviewed research articles and has served on the editorial boards of several leading journals of statistics including the Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series B) and Statistical Science.
Donald B. Rubin is an Emeritus Professor of Statistics at Harvard University, and is currently affiliated with Temple University. He is most well known for the Rubin causal model, a set of methods designed for causal inference with observational data, and for his methods for dealing with missing data. Professor Rubin is a fellow/member/honorary member of the Woodrow Wilson Society, Guggenheim Memorial Foundation, Alexander von Humboldt Foundation, American Statistical Association, Institute of Mathematical Statistics, International Statistical Institute, American Association for the Advancement of Science, American Academy of Arts and Sciences, European Association of Methodology, the British Academy, and the US National Academy of Sciences. He has authored or co-authored about 450 publications (including 10 books), has four joint patents, and is one of the most highly cited authors in the world, with nearly 450,000 citations.
