Machine Learning and Data Science for Economists


February 12-13, 2024

2 days, 8:30 AM – 4:30 PM
NABE Conference Center
Washington, DC

This course introduces applied economists to new analytical methods, which lie at the intersection of traditional statistics, machine learning, and computer science, from the perspective of econometric analysis.  More course information


NABE Member: $1,600

U.S. Government Employee: $1,675

Non-Member: $1,750


Or download the registration form:

and email to [email protected]. Please note: a $25 processing fee will be added to registrations not completed online.

To be eligible for a refund less $50 fee, registration cancellation must be received in writing by January 10, 2024.  Refunds will not be permitted past that date. Registration transfers can be considered upon request; however, a $75 processing fee will be assessed for all registration transfers. NABE reserves the right to cancel the seminar if sufficient registration is not achieved. Questions? Please contact NABE at [email protected] or phone 202-463-6223.


NABE Conference Center
1020 19th Street, NW
Washington, DC 20036



Capital Hilton
1001 16th Street, NW
Washington, DC 20036

NABE has secured a discounted room block ($269/night) at the Capital Hilton for course attendees. To make a reservation, book online here. The room block deadline is January 19, or when the block reaches capacity, whichever comes first.

About the Instructor:

Brian Quistorff is Chief Data Scientist at the Bureau of Economic Analysis (BEA). Previously, he was a Research Economist in the Office of the Chief Economist at the BEA. Prior to joining the BEA, he was an Economist in the Office of the Chief Economist in the AI + Research division of Microsoft. This group combined Economists with traditional Data Scientists to address difficult challenges facing Microsoft. He worked across many products groups, including Office and Gaming, and on external engagements, including with the World Bank. He specializes in embedding Machine Learning into existing Econometric methods to both improve the quality of estimation/causal inference and to save time/reduce errors in model selection. He also works to open source generic tools to benefit the broader analytics community. He holds a PhD in Economics from the University of Maryland, a MA of Economics from the University of British Columbia, and a BS in Computer Science from Stanford University.