When can we address unobserved confounding in policy evaluation? Conversely, how can we create predictive models that are robust to hidden confounders?
Can model selection be used in identifying the best economic models from data?
How can we use machine learning to improve generalized method of moments (GMM) and conversely, can moment selection improve machine learning?
How do we model and empirically calibrate economic, social, and ethical concerns that arise from the use of machine learning models in the real world?
How do we leverage machine learning models when the ultimate goal is economic performance such as revenue or welfare?
How can we apply semi-parametric models in machine learning?
How can we apply recent advances in offline reinforcement learning to policy evaluation and learning in economics?
Can econometric techniques such as instrumental variables, difference-in-difference, and regression discontinuities be used to improve machine learning pipelines?
How can machine learning improve computationally difficult traditional econometric estimation such as dynamic discrete choice?
will be the topic of our panel discussion. Work in machine learning aspires towards studying domains such as economic systems, education, and labor markets. The complexity of evaluating social and economic programs highlight shortcomings of current approaches in ML and opportunities for methodological innovation. These challenges include more complex environments (markets, equilibrium, temporal considerations) and those emerging from human behaviour (heterogeneity, delayed effects, unobserved confounders, strategic response).
John Hopkins Bloomberg School of Public Health
The Wharton School
University of Pennsylvania
Department of Economics
Yale University
Department of Economics
UC Berkeley
New York University
Microsoft Research
Stanford University
Anna Korba
Ashesh Rambachan
Ben Deaner
Brad Ross
David Ritzwoller
David Watson
Dmitry Arkhangelsky
Elena Manresa
Hadi Elzayn
Jann Spiess
Jantje Sönksen
Jason Hartford
Jiafeng Chen
Jonathan Roth
Julius von Kügelgen
Martin Huber
Michel Besserve
Mladen Kolar
Panos Toulis
Patrick Burauel
Petros Dellaportas
Philip Erickson
Rahul Singh
Ruoxuan Xiong
Stephen Hansen
Timothy Christensen
Xinkun Nie
Yuchen Zhu
Yusuke Narita
Zhaonan Qu