Project Grant 2515262
- This $175,000 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support research on the problem of external validity in causal inference under interference. The project aims to understand how causal conclusions from a sample can be generalized to a broader population, when treatment and outcome spillover effects exist. Key deliverables include software for public use and workshops for high school students, in addition to...
- This $350,000 federal Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support the development of new theory and methods for assessing the sensitivity of causal inferences to violations of underlying assumptions in quasi-experimental research designs. The research project aims to create a comprehensive suite of sensitivity analysis tools for popular causal inference techniques such as instrumental variables,...
- This National Science Foundation project grant of $281,469 will fund research at the University of Colorado Denver from August 2022 through July 2025 to advance causal inference methods for heterogeneous data fusion. Specifically, the award will support developing new approaches to empirically estimate associations and perform causal inference when individual-level data cannot be obtained due to privacy or logistical constraints. The research aims to extend statistical methodologies to...
- The National Science Foundation awarded $150,000 to Regents of the University of California at Riverside under the Mathematical and Physical Sciences program (CFDA 47.049) from July 1, 2023 to June 30, 2026. The Project Grant funding will support research to develop new statistical methodologies and deep learning techniques for uniformly estimating causal effects of continuous treatments using large observational health data sets. Specifically, the university will design neural network...
- The National Science Foundation (NSF) awarded a $125,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Washington (UW) for a collaborative research project titled "Theory of Causal Learning: How Can We Interpret Results from Complex Machine Learning Algorithms? How Can We Mitigate the Risks Associated with Using Such Models for Policy Decisions?". The project aims to establish a theoretical foundation for causal learning that...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $200,000 Project Grant to The University Corporation, a non-profit organization located in Northridge, CA. The grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), focuses on developing new statistical modeling and data resampling methods to address challenges posed by incomplete, missing, and fragmented observations in large datasets. Key objectives include: Advancing...
- This $155,000 project grant awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) will develop new simulation-based inference (SBI) methods. These innovations aim to empower scientists to make better use of complex models across diverse domains such as genetics, ecology, biology, economics, and psychology, supporting more scalable, efficient, and reliable decision-making. The project will address two core challenges...
- This $160,000 National Science Foundation project grant supports research at Cornell University to develop cutting-edge machine learning methods for causal inference with high-dimensional complex data through 2026. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the research aims to address theoretical, methodological and computational challenges of drawing causal conclusions from vast datasets. Specific projects include proposing a covariate balancing methodology...
- This $155,372 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will fund research to develop advanced statistical methods for extracting insights from high-dimensional, high-frequency "big data." The University of Illinois, Chicago, as the prime awardee, will focus on four key areas: 1) advancing contiguity theory to enable more robust statistical analysis of noisy, high-frequency data; 2) exploring time-varying...
- This $174,118 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop new algorithms and software tools to enable robust causal inference from observational data, even when faced with model misspecification and uncertainty. The project seeks to build methods that allow data scientists to propose multiple causal models and combine effect estimates, as well as perform model selection that...
The National Science Foundation (NSF) awarded a $108,000 Project Grant under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to the University of Illinois for the collaborative research project "Distributional Balancing Methods for Advancing Causal Inference in Complex Settings". The project aims to develop advanced statistical methodologies that improve the reliability of causal conclusions from complex, observational data. Specifically, it will enhance distributional balancing techniques to account for data dependency structures and unobserved confounding, enabling more trustworthy evidence for decision-making in healthcare, education, economics, and environmental policy. The research outcomes will include open-source software, user-friendly resources, and student training in cutting-edge statistical methods. The project period is from September 1, 2025 to August 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $108.0k | 8/7/25 |