NSF Org:	SES Division of Social and Economic Sciences
Recipient:	THE LELAND STANFORD JUNIOR UNIVERSITY
Initial Amendment Date:	August 14, 2023
Latest Amendment Date:	July 31, 2024
Award Number:	2242876
Award Instrument:	Continuing Grant
Program Manager:	Cheryl Eavey ceavey@nsf.gov  (703)292-7269 SES  Division of Social and Economic Sciences SBE  Directorate for Social, Behavioral and Economic Sciences
Start Date:	September 1, 2023
End Date:	August 31, 2026 (Estimated)
Total Intended Award Amount:	$449,098.00
Total Awarded Amount to Date:	$449,098.00
Funds Obligated to Date:	FY 2023 = $444,484.00 FY 2024 = $4,614.00
History of Investigator:	Stefan Wager (Principal Investigator) swager@stanford.edu
Recipient Sponsored Research Office:	Stanford University 450 JANE STANFORD WAY STANFORD CA  US  94305-2004 (650)723-2300
Sponsor Congressional District:	16
Primary Place of Performance:	Stanford University 450 Jane Stanford Way Stanford CA  US  94305-2004
Primary Place of Performance Congressional District:	16
Unique Entity Identifier (UEI):	HJD6G4D6TJY5
Parent UEI:
NSF Program(s):	Methodology, Measuremt & Stats
Primary Program Source:	01002324DB NSF RESEARCH & RELATED ACTIVIT 01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	9179
Program Element Code(s):	133300
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.075

ABSTRACT

This research project will develop new methods and software for data-driven decision making under environmental shift. Most work in data-driven decision making fundamentally relies on the stability of the statistical environment. Available tools for data-driven decision making generally assume that future environments in which decision rules will be deployed resemble the past environment where data was collected. Many real-world applications, however, display significant environmental shift due to various factors. The newly developed methods will expand researchers' ability to apply data-driven decision making to systems with environmental shift. Possible application areas range from medical settings and social programs to online marketplaces. Educational activities will include the training of graduate students and the development of learning resources based in part on the results of this research. All research products will be disseminated via publicly available repositories, and software will be released under an open-source license.

This research project will provide a practical deep learning-based framework for learning decision rules that is robust to unknown distributional shifts. The project will develop methods for learning decision rules in settings with unknown distributional shifts, and where some sub-populations may be under- or over-sampled according to unobservable characteristics. Consider, for example, a volunteer-based study on the effects of antidepressants among patients suffering from depression, where motivation to take steps to fight depression could be an unobserved attribute that is overrepresented in the study population ? and so the set of people we are able to collect data on may differ from the full patient population along some important but unobservable attributes. The project also will investigate learning decision rules under distributional shifts that naturally arise via equilibrium behavior in social settings with agents who interact with each other (e.g., by buying and selling goods to each other in a marketplace) and provide methods to address the resulting challenges. Overall, this project will provide new methodological and software solutions ? as well as associated educational resources ? that will expand the class of problems where data-driven decision making can be successfully deployed.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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