I have written a paper for economists that describes some of the techniques from machine learning that can be helpful for econometric analysis. This talk is an attempt to look at the reverse issue: what techniques from econometrics may be helpful for machine learning. One set of issues involves non-IID data such as time series and panel data. But the most important set of issues involves estimating causal effects, where seemingly straightforward approaches lead to serious estimation problems. Luckily there are a number of econometric techniques that can be applied to deal with these problems, which I will outline in the talk.
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About the speaker:
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.
Professor Varian has published numerous papers in economic theory, industrial organization, financial economics, econometrics and information economics. He is the author of two major economics textbooks which have been translated into 22 languages. He is the co-author of a bestselling book on business strategy, Information Rules: A Strategic Guide to the Network Economy and wrote a monthly column for the New York Times from 2000 to 2007.
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