Donald Rubin's work connects Causal inference, missing data and Bayesian statistics. His Harvard profile lists Causal inference in experiments and observational studies, nonresponse in sample surveys, and Bayesian and empirical Bayesian methods as research interests.[ref]
The potential outcomes framework makes a causal comparison explicit by distinguishing the outcomes that would occur under alternative treatments. Only the outcome under the received treatment is observed for each individual, so identifying an average treatment effect requires design or modelling assumptions. In missing-data analysis, multiple imputation propagates uncertainty by analyzing several completed datasets and pooling their results with Rubin's rules. These ideas explain why filling in one convenient value and then treating it as observed generally understates uncertainty.
Portrait of Donald B. Rubin
. Photograph of Donald B. Rubin linked from the Harvard Department of Statistics profile and labelled Don Rubin.- Maximum Likelihood from Incomplete Data via the EM Algorithm by Arthur Pentland Dempster and others
- Principal Stratification in Causal Inference by Constantine E. Frangakis and Donald B. Rubin
- The central role of the propensity score in observational studies for causal effects by Paul R. Rosenbaum and Donald B. Rubin
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Donald Rubin is an American statistician widely known for his contributions to the field of causal inference and statistics. He is best known for the development of the Rubin Causal Model (RCM), which provides a framework for understanding causal relationships through the use of potential outcomes. This model emphasizes the importance of randomization in experimental design and has significant implications for fields such as economics, social sciences, and epidemiology.