# Collaborative Research: Causal Learning with High-dimensional Imaging Outcomes: Methods, Theory, and Algorithms

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Iowa State University (IA) · $125,627

## Abstract

The analysis of imaging outcomes is a dynamic and rapidly evolving research field, driven by the growing accessibility of large-scale biomedical imaging databases. Imaging data, often characterized as functional data, presents unique opportunities and challenges for statistical analysis. Existing methods, however, are insufficient for handling the computational demands of large-scale medical imaging data or addressing issues such as unmeasured confounding and population heterogeneity in causal analysis. This research will develop advanced statistical tools to overcome these critical hurdles. By developing new techniques that efficiently process large-scale imaging information and provide more accurate causal insights, this work will advance national interests in scientific innovation and evidence-based decision-making. It will promote scientific progress in a vital area of imaging data analysis and aims to advance public health by enabling a deeper understanding of treatment effects from observational studies. The developed data analytics tools also have broad applicability across various fields, including aging research, digital health, and plant science, addressing challenges faced by modern society. Furthermore, the project will benefit the broader research community through the release of freely available software tools and will support STEM education by involving undergraduate and graduate students in hands-on research and integrating project findings into curriculum dev

## Key facts

- **NSF award ID:** 2515789
- **Awardee organization:** Iowa State University (IA)
- **SAM.gov UEI:** DQDBM7FGJPC5
- **PI:** Peng Liu
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Machine Learning Theory, EXP PROG TO STIM COMP RES
- **Estimated total:** $125,627
- **Funds obligated:** $125,627
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2515789

## Citation

> US National Science Foundation, Award 2515789, Collaborative Research: Causal Learning with High-dimensional Imaging Outcomes: Methods, Theory, and Algorithms. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2515789. Licensed CC0.

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