Machine learning and econometrics

Apr. 23, 2014 from 3:30 to 4:20 p.m. — Johnson Hall, room 102

Chief Economist, Google


Machine learning is mostly concerned about prediction, while econometrics is mostly concerned about causal inference for economic behavior. Although the gold standard for causality is randomized experiments, there are practical techniques for analysing observational data that can yield some insight about causal effects. In this talk I discuss how these techniques may be useful in a machine learning context.


Hal R. Varian is the Chief Economist at Google. He started in May 2002 as a consultant and has been involved in many aspects of the company, including auction design, econometric analysis, finance, corporate strategy and public policy. He is also an emeritus professor at the University of California, Berkeley in three departments: business, economics, and information management. He received his SB degree from MIT in 1969 and his MA in mathematics and Ph.D. in economics from UC Berkeley in 1973. He has also taught at MIT, Stanford, Oxford, Michigan and other universities around the world. Dr. Varian is a fellow of the Guggenheim Foundation, the Econometric Society, and the American Academy of Arts and Sciences. He was Co-Editor of the American Economic Review from 1987-1990 and holds honorary doctorates from the University of Oulu, Finland and the University of Karlsruhe, Germany.