Donald Rubin
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Donald Bruce Rubin is the John L. Loeb Professor of Statistics at Harvard University. He was hired by Harvard in 1984, and served as chair of the department from 1985-1994.

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.

As an undergraduate Rubin attended the accelerated Princeton University PhD program where he was one of a cohort of 20 students mentored by the physicist John Wheeler (the intention of the program was to confirm degrees within 5 years of freshman matriculation). He switched to psychology and graduated in 1965. He began graduate school in psychology at Harvard with a National Science Foundation fellowship, but because his statistics background was considered insufficient, he was asked to take introductory statistics courses. Rubin felt insulted by this given his background in physics, so he decided to transfer to applied math, as he says in the introduction to Matched Sampling for Causal Effects.[citation needed]

He received his AM in applied math in 1966, and spent the summer consulting for Princeton sociologist Robert Althauser on comparing the achievement of white and black students, where he first used a matching method[citation needed].

Rubin became a PhD student again, this time in Statistics under William Cochran at the Harvard Statistics Department. After graduating from Harvard in 1970, he began working at the Educational Testing Service in 1971, and served as a visiting faculty member at Princeton's new statistics department. He published his major papers on the Rubin Causal Model in 1974-1980, and a textbook on the subject with econometrician Guido Imbens is expected to be published in 2010.

Rubin later moved to the University of Wisconsin–Madison, the University of Chicago, and Harvard.


invited talk
flag Direct and indirect causal effects: a helpful distinction?
as author at  International Conference on Applied Statistics 2010,
flag Taking causality seriously: Propensity score methodology applied to estimate the effects of marketing interventions - Best PKDD paper
as author at   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Dubrovnik 2003,