Donald Rubin is an American statistician known for his work in causal inference and experimental design. He is best known for developing the Rubin Causal Model, which provides a framework for understanding causal relationships in statistics. His contributions have significantly influenced fields such as social science, medicine, and economics. Rubin has also been involved in the development of propensity score matching, a technique used to reduce bias in observational studies. He has published numerous papers and books, making him a prominent figure in the field of statistics. His work continues to impact how researchers design studies and interpret data.