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Event Recap

Machine learning techniques are being actively pursued in the private sector and have been widely adopted in fields such as computational biology and computer vision. However, the role of machine learning in economics has so far been limited. This workshop was organized to provide a forum to discuss how ideas and techniques from machine learning could be applied to economic questions. The workshop will bring together researchers from computer science, statistics, econometrics and applied economics to foster interactions and discuss different perspectives on statistical learning and its potential impact on economics.

The workshop began with overview talks on machine learning and statistics by researchers from outside of economics. Three following sessions were organized around the themes of causal inference, prediction, and networks and complex data. Each session included the presentation of papers in economics that make use of machine learning methodology, followed by a discussion by researchers from multiple communities.


Friday, September 23, 2016
Overview Talk 1
David Blei, Columbia University
Overview Talk 2
Larry Wasserman, Carnegie Mellon University
Contributed Talk 1: Prediction
Matthew Shou-Chung Shum , California Institute of Technology
Francis Diebold, University of Pennsylvania
Discussant: Serena Ng
Contributed Talk 2: Causal Inference
Victor Chernozhukov, Massachusetts Institute of Technology
Susan Athey, Stanford Graduate School of Business
Mladen Kolar, Associate Professor of Econometrics and Statistics, Booth School of Business
Saturday, September 24, 2016
Overview Talk 3
Jon Kleinberg, Cornell University
Contributed Talk 3: Networks and Complex Data
Matthew Gentzkow, Professor of Economics, Stanford University
Aureo de Paula, University College London
Discussant: Edoardo Airoldi