Introduction to Modern Randomization-Based Design and Analysis for Causal Inference

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.

• 1. Understanding experimental design: fundamental concepts 2. CRD with one factor, two levels 3. Better comparisons using blocking 4. Beyond blocking: acceptable versus unacceptable allocations 5. Randomized experiments with J(> 2) treatment arms 6. The 22 factorial experiment 7. 2K factorial designs 8. Design and analysis of factorial experiments with constraints 9. Model-based analysis of designed experiments and superpopulation inference 10. Including covariates in the design, model-based analysis and subsequent inference 11. Appendix Bibliography