Essential Concepts of Causal Inference: A Remarkable History

Abstract

[evdate 2018-07-25] Title from presentation title slide: Essential concepts of causal inference in randominzed experiments and observational studies: a remarkable history.

IAS Distinguished Lecture.

Title from opening screen.

Abstract: The speaker believes 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, to him at least, was tied to ideas that were developing in quantum mechanics. The key ideas of randomized experiments evidently were not applied to the 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 in economics. However, humans are more complex than plants and animals. 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, include the bridging of classical mathematical ideas of experimentation, the fractional replication and geometrical formulations from the early twentieth century, and modern ideas that rely on powerful computing to implement many of the tedious aspects of design and analysis.

About the speaker: Prof Donald B Rubin received his PhD in Statistics from Harvard University in 1970. He joined the Educational Testing Service in Princeton before moving to the University of Chicago as a Professor in 1982. In 1984, he returned to Harvard and is currently the John L Loeb Professor of Statistics.

Prof Rubin's research focuses on causal inference in experiments and observational studies, inference in sample surveys with nonresponse and in missing data problems, application of Bayesian and empirical Bayesian techniques, and developing and applying statistical models to data in a variety of scientific disciplines.

Prof Rubin was elected a fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the American Statistical Association, the Institute for Employment Research, Germany and the Institute of Mathematical Statistics. He was also elected a member of the American Public Health Association, the International Association of Survey Statisticians, the US National Academy of Sciences, the Royal Statistical Society and honorary member of the European Association of Methodology. As of 2017, he has authored/co-authored over 400 publications and has four joint patents. He has been one of the most highly cited authors in the world for many years.

Duration: 96 min.