Machine Learning and Data Science for Economists


March 23-24, 2022

2 days, 8:30 AM – 4:30 PM
The Ritz-Carlton Hotel
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

Please note: Proof of COVID-19 vaccination is required of all participants. Participants will also be required to follow any District of Columbia masking guidelines that are in effect at the time of the course. 


NABE Member: $1,600

U.S. Government Employee: $1,675

Non-Member: $1,750



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 March 2, 2022.  Questions? Please contact NABE at [email protected] or phone 202-463-6223.


The Ritz-Carlton, Washington, DC
1150 22nd Street, NW
Washington, DC 20037

NABE has secured a block of rooms for rate of $299/night. 

Book Room

Should you need a room, please book using the above link or call the hotel directly at (202) 835-0500 and mention the group rate.  BOOK EARLY! The rate is available until February 23, 2022, or until the room block is full, whichever occurs first. 


About the Instructor:

Brian Quistorff is an Economist in the Office of the Chief Economist in the AI + Research division of Microsoft. This group combines Economists with traditional Data Scientists to address difficult challenges facing Microsoft. He has 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.