Machine Learning: What’s in it for Economics?

September 23–24, 2016

(All day)

Saieh Hall for Economics, Room 021
Organizers
Stéphane Bonhomme, University of Chicago
Lars Peter Hansen, University of Chicago
John Lafferty, University of Chicago
Thibaut Lamadon, University of Chicago

Featured Media Playlist

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.

In this article, conference organizers shared their thoughts on the value of sharing techniques across economics, statistics, econometrics, and data science here; institute director also explores those connections here.

 

September 23, 2016 (All day) September 24, 2016 (All day)