Project Abstract/Summary
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.
Principal Investigator
Stefan Wager – Stanford University located in STANFORD, CA
Co-Principal Investigators
Funders
Funding Amount
$449,098.00
Project Start Date
09/01/2023
Project End Date
08/31/2026
Will the project remain active for the next two years?
The project has more than two years remaining
Source: National Science Foundation
Please be advised that recent changes in federal funding schemes may have impacted the project’s scope and status.
Updated: April, 2025