Donald B. Rubin

Donald B. Rubin is a renowned statistician known for his work in developing methods to analyze data from studies where participants are not randomly assigned to groups, such as observational studies. His most influential contribution, the Rubin Causal Model, provides a framework to estimate and understand cause-and-effect relationships by accounting for biases and confounding factors. This approach helps researchers draw more accurate conclusions about how different treatments or interventions impact outcomes, making it a foundational concept in fields like medicine, social sciences, and economics.