lectures

donald rubin speaking

Essential Concepts of  Causal Inference - A Remarkable History

Donald B. Rubin
John L. Loeb Professor of Statistics, Harvard University

Tuesday, December 5, 2017
3:00 p.m.

Transportation Center, 600 Foster Street

Reception to follow

Abstract
I believe that a deep understanding of cause and effect, and how to estimate causal effects from data, complete with the associated mathematical notation and expressions, only evolved in the twentieth century.  The crucial idea of randomized experiments was apparently first proposed in 1925 in the context of agricultural field trails but quickly moved to be applied also in studies of animal breeding and then in industrial manufacturing.  The conceptual understanding seemed to be tied to ideas that were developing in quantum mechanics.  The key ideas of randomized experiments evidently were not applied to studies of human beings until the 1950s, when such experiments began to be used in controlled medical trials, and then in social science — in education and economics.  Humans are more complex than plants and animals, however, and with such trials came the attendant complexities of non-compliance with assigned treatment and the occurrence of Hawthorne and placebo effects. The formal application of the insights from earlier simpler experimental settings to more complex ones dealing with people, started in the 1970s and continue to this day, and include the bridging of classical mathematical ideas of experimentation, including fractional replication and geometrical formulations from the early twentieth century, with modern ideas that rely on powerful computing to implement aspects of design and analysis.

Donald B. Rubin is the John L. Loeb Professor of Statistics at Harvard University. Professor Rubin’s work in causal inference, missing data, matching and applied Bayesian inference essentially defined new fields of statistics. His methods are now embedded in statistical software used by virtually all empirical scientists, and his books “Statistical Analysis with Missing Data,” “Multiple Imputation for Nonresponse in Surveys,” “Matched Sampling for Causal Effects” and “Applied Bayesian Inference” are essential reference works. Read more about him at: statistics.fas.harvard.edu/people/donald-b-rubin

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