Machine Learning and Data Science for Economists


March 27-28, 2023

2 days, 8:30 AM – 4:30 PM
NABE Office 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

Early-Bird Registration*:

NABE Member: $1,500

U.S. Government Employee: $1,575

Non-Member: $1,650

*Early-Bird Deadline: February 22, 2023


Or download the registration form and email to [email protected] Please note that a $25 processing fee will be added.

NOTE: To be eligible for a refund less $50 fee, registration cancellation must be received in writing by February 22, 2022. 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 202-463-6223.


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

Need accommodations? NABE has secured a block of rooms at the Capital Hilton (1001 16th Street, NW, Washington, DC 20036) for the discounted rate of $299/night (plus tax). To make a reservation online, book here. To make a reservation by phone, please call 800-405-0064 and mention code: NAB.

Please note: The room block deadline is March 1, or when the block reaches capacity, whichever comes first.

About the Instructor:

Brian Quistorff is a Research Economist at the Bureau of Economic Analysis. Formerly, 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.