"Machine Learning: An Applied Econometric Introduction"Monday, 8/16, 10:20am CT
Summer Institute in Machine Learning in Economics (MLESI21)
August 16-20, 2021
“Dynamic Experimentation and Bandit Approaches in Economic Research”Monday, 8/16, 2pm CT
“Applications of Machine Learning for Development: Measuring Poverty”Wednesday, 8/18, 2pm CT
“Converting Complex Raw Documents to Computable Structured Data”Thursday, 8/19, 11am CT
“Partisan Speech: Measuring Group Differences in High-Dimensional Choices”Thursday, 8/19, 2pm CT
"Causal Panel Data Models and Matrix Completion Methods"Friday, 8/20, 11am CT
“Machine Learning, General Purpose Technologies, and the Role of Economists”Friday, 8/20, 1pm CT
From August 16–20, 2021, CAAI will host the first Summer Institute in Machine Learning in Economics (MLE). It is organized by Jens Ludwig (Chicago), Sendhil Mullainathan (Chicago), Ziad Obermeyer (Berkeley), and Jann Spiess (Stanford). Modeled after similar programs in behavioral economics, the Institute will bring together leading researchers with advanced graduate students and junior faculty every year; this year, due to Covid-19, the Institute will be held virtually, as a mix of public lectures and small-group sessions. Visiting faculty this year will include Susan Athey and Guido Imbens, as well as other senior faculty members bridging computer science, statistics, and economics.
Goal of the program:
1. to introduce researchers to the new methods and data used in machine learning (e.g., supervised and unsupervised learning algorithms, machine vision, text analysis), and how they relate to longstanding questions around causal inference, experimental design, fairness and bias, and econometric theory; and
2. to build intuitions for how these methods can open up new questions for economists in fields like macroeconomics, behavioral science, health, development, public, labor, or applied microeconomics generally.
In addition to public lectures and panel discussions with faculty members, which will be open to the public this year, we will bring participants and faculty together in (virtual) social events and mentoring sessions in smaller groups.
- Susan Athey, Professor of Economics, Stanford Graduate School of Business
- Hamsa Bastani, Assistant Professor of Operations, Information and Decisions, Wharton School of Management
- Joshua Blumenstock, Associate Professor, Berkeley School of Information
- Amitabh Chandra, Henry and Allison McCance Professor of Business Administration, Harvard Business School
- Victor Chernozhukov, Professor, MIT Department of Economics
- Bo Cowgill, Assistant Professor of Management, Columbia Business School
- Melissa Dell, Professor of Economics, Harvard University
- Matthew Gentzkow, Professor of Technology and the Economy, Stanford
- Avi Goldfarb, Professor of Marketing, Rotman School of Management
- Guido Imbens, Professor of Economics, Stanford Graduate School of Business
- Danielle Li, Associate Professor, Technological Innovation, Entrepreneurship, and Strategic Management, Sloan School of Management
- Annie Liang, Assistant Professor of Economics, Northwestern University
Participation in the full program will be by application, and limited to around 30 Ph.D. students (who will have completed one year of their program by summer 2021) and junior faculty (who have completed their Ph.D. program after 2018). Candidates in economics and related disciplines (e.g., computer science, statistics, etc.) who have a strong interest and advanced training in formal economics are eligible.
Applications Closed. MLESI22 applications will open in the Winter/Spring.
Edwin A. and Betty L. Bergman Distinguished Service Professor, Harris School of Public PolicyJens Ludwig
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