The Federal Reserve Bank of Philadelphia is hosting a conference on Frontiers in Machine Learning and Economics: Methods and Applications on October 7-8, 2022. The goal of the conference is to bring together leading researchers across fields that work at the intersection of machine learning and the social sciences.

Examples of potential topics include (but are not limited to):

  • Methodological advances in analyzing complex and high-dimensional data sets (e.g. likelihood-free Bayesian computation, benign overfitting).
  • Methodological advances in natural language processing, in particular with respect to causal inference.
  • Applications of state-of-the-art natural language processing or other machine learning methods in economics and the social sciences more broadly.
  • Methodological advances in using machine learning for causal inference.

Papers using both observational and experimental data, and any approaches to estimating counterfactuals are welcomed. We also welcome submissions from fields outside of economics that use methods and data that are of interest to economists. There are no parallel sessions, and we aim to include about 10 papers in the program and assign discussants.

Confirmed Plenary speakers:

  • Margaret Roberts (University of California - San Diego)
  • Christian Robert (Université Paris-Dauphine and University of Warwick ), "How Approximate Are Approximate Bayesian Computation Methods?"

Submissions

Completed manuscripts (including early drafts) should be submitted no later than June 15, 2022. They can be submitted by email to Phil.Machine.Learning.Initiative@phil.frb.org. Submissions should be in PDF and indicate the presenting author. Authors of accepted papers will be notified by mid-July 2022.

For any questions, please contact Phil.Machine.Learning.Initiative@phil.frb.org.

Organizing Committee