December 13th, 2021
Workshop on Machine Learning meets Econometrics (MLECON)
co-locate with Neural Information Processing Systems (NeurIPS 2021)
A one-day workshop consisting of invited talks, shorter contributed talks for selected papers, an interdisciplinary panel discussion, and Gather.Town poster sessions.
A synergy between machine learning (ML) and econometrics (ECON) has created a growing body of works in several directions including nonparametric instrumental variable regression, proximal causal learning, double machine learning, adaptive allocation in economic systems and program evaluation, among others. In particular, there is great interest in ML for econometrics. ML for economics offers the potential for better predictions and handling larger, multimodal data to help address substantive questions in economics and the social sciences. Non- and semi-parametric econometrics naturally interface with modern machine learning. On the other hand, incorporating economic modeling of microfoundations can also improve machine learning’s impact in societal systems and policy-relevant questions.
The MLECON workshop aims to serve as an interface for experts from both disciplines to meet and exchange ideas and for participants to present their works-in-progress that lies at this intersection. We welcome both extended abstract and technical papers on topics that shed light on the area, including (but not limited to) the following questions:
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?
Machine Learning in Social Systems: Challenges and Opportunities from Program Evaluation
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).
Confirmed Speakers and Panelists
Elizabeth A. Stuart
John Hopkins Bloomberg School of Public Health
Eric Tchetgen Tchetgen
The Wharton School
University of Pennsylvania
Xiaohong Chen
Department of Economics
Yale University
Vira Semenova
Department of Economics
UC Berkeley
Jennifer Hill
New York University
Vasilis Syrgkanis
Microsoft Research
Guido Imbens
Stanford University
Important Dates
September 1st, 2021
October 1st, 2021
October 23rd, 2021
December 13th, 2021
Submission opens
Paper submission deadline
Author notification
Main workshop
Venue
MLECON2021
The MLECON2021 workshop will be entirely virtual.
Organization
UC Berkeley
Gatsby Unit, University College London
University of Oxford/Alan Turing Institute
Helmholtz AI Munich
Max Planck Institute for Intelligent Systems
Stanford University
Cornell University
Program Committee
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