2515303
Project Grant
Overview
Grant Description
Collaborative research: Generalized fiducial inference in complex systems: Causal inference, networks, and normalizing flows.
Accurate statistical inference is essential for making reliable decisions in various fields, such as forensic science, medicine, economics, and machine learning.
This project develops and advances generalized fiducial inference (GFI), an innovative statistical method that quantifies uncertainty without requiring subjective assumptions.
By addressing complex real-world problems, such as evaluating evidence in criminal cases, understanding causal relationships in economics and health, and improving reliability in machine learning, the project will significantly enhance decision-making processes.
Additionally, the project provides valuable research training opportunities for graduate students in science, technology, engineering, and mathematics (STEM), thereby contributing directly to national goals of promoting scientific advancement, health, prosperity, and welfare.
This collaborative research aims to advance generalized fiducial inference (GFI), building upon Fisher’s original fiducial argument and recent developments in modern statistics.
The primary objectives include extending GFI methods to causal inference models, particularly instrumental variable models, and redefining GFI through normalizing flows to manage computational complexity in non-analytic scenarios.
The project will also apply these methodological innovations to pressing real-world problems in forensic science, specifically addressing the accurate calibration of likelihood ratios from machine learning models, as well as, resources permitting, investigations into uncertainty quantification for social network learning and sports analytics.
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.
Subawards are not planned for this award.
Accurate statistical inference is essential for making reliable decisions in various fields, such as forensic science, medicine, economics, and machine learning.
This project develops and advances generalized fiducial inference (GFI), an innovative statistical method that quantifies uncertainty without requiring subjective assumptions.
By addressing complex real-world problems, such as evaluating evidence in criminal cases, understanding causal relationships in economics and health, and improving reliability in machine learning, the project will significantly enhance decision-making processes.
Additionally, the project provides valuable research training opportunities for graduate students in science, technology, engineering, and mathematics (STEM), thereby contributing directly to national goals of promoting scientific advancement, health, prosperity, and welfare.
This collaborative research aims to advance generalized fiducial inference (GFI), building upon Fisher’s original fiducial argument and recent developments in modern statistics.
The primary objectives include extending GFI methods to causal inference models, particularly instrumental variable models, and redefining GFI through normalizing flows to manage computational complexity in non-analytic scenarios.
The project will also apply these methodological innovations to pressing real-world problems in forensic science, specifically addressing the accurate calibration of likelihood ratios from machine learning models, as well as, resources permitting, investigations into uncertainty quantification for social network learning and sports analytics.
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.
Subawards are not planned for this award.
Funding Goals
THE GOAL OF THIS PROGRAM IS TO SUPPORT RESEARCH PROPOSALS SPECIFIC TO "STATISTICS
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Chapel Hill,
North Carolina
27599-5023
United States
Geographic Scope
Single Zip Code
Related Opportunity
University Of North Carolina At Chapel Hill was awarded
Project Grant 2515303
worth $125,000
from the Division of Mathematical Sciences in September 2025 with work to be completed primarily in Chapel Hill North Carolina United States.
The grant
has a duration of 3 years and
was awarded through assistance program 47.049 Mathematical and Physical Sciences.
The Project Grant was awarded through grant opportunity Statistics.
Status
(Ongoing)
Last Modified 8/21/25
Period of Performance
9/1/25
Start Date
8/31/28
End Date
Funding Split
$125.0K
Federal Obligation
$0.0
Non-Federal Obligation
$125.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2515303
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Public/State Controlled Institution Of Higher Education
Awarding Office
490304 DIVISION OF MATHEMATICAL SCIENCES
Funding Office
490304 DIVISION OF MATHEMATICAL SCIENCES
Awardee UEI
D3LHU66KBLD5
Awardee CAGE
4B856
Performance District
NC-04
Senators
Thom Tillis
Ted Budd
Ted Budd
Modified: 8/21/25