Project Grant 2515763
Award Date 9/1/25
Completion Date 8/31/28
Dollars Obligated $175K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Penn State University, PA 16802, USA
Similar Awards
- This Project Grant award, funded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Program (CFDA 47.049), supports research on causal inference and interference in statistical experiments. The $155,146 award to Carnegie Mellon University aims to study how interference - such as social influence, economic competition, or information sharing - complicates statistical analysis and weakens conclusions drawn from experiments. The project will extend methods for...
- 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...
- The National Science Foundation (NSF) awarded a $155,647 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to The Pennsylvania State University (Penn State). The award, with a project period from July 15, 2025 to June 30, 2028, supports research to develop statistical methods for understanding and quantifying the influence of network interactions on individual behavior and status. Key objectives include designing a data-driven framework for estimating...
- 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 Project Grant award of $194,675 from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to develop new econometric and statistical methods that allow researchers to draw valid conclusions from complex data in a variety of social, behavioral, and medical science settings. The key products and services to be delivered include: Developing a generally applicable reparameterization procedure for informative...
- 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...
- 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 (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...
- This Project Grant award of $179,999 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports comprehensive statistical and computational analyses with the goal of advancing innovative nonparametric data analysis techniques. The research aims to push the boundaries of modern nonparametric statistical inference and develop methodologies applicable to areas such as latent variable models, time series analysis, and sequential nonparametric...
- 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 $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 advancing the state of knowledge on this open problem in causal inference. The award was made to The Pennsylvania State University, a major public research institution, and will be executed over a 3-year period from September 2025 through August 2028.
Generated 10/21/25, 4:55 AM
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $175.0k | 8/14/25 |