Project Grant 2515792
- 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 $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 $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 $250,000 Project Grant was awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to Carnegie Mellon University (CMU). The project aims to develop flexible, valid inference procedures for modern complex data that leverage powerful black-box machine learning algorithms. Key focus areas include novel variants of cross-validation to enable adaptive inference, as well as performance guarantees of cross-validation for...
- 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...
- This $200,000 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) will support the development of new statistical methods that incorporate qualitative constraints into semi-parametric models. The awardee, Carnegie Mellon University, will work to create general non-parametric regression estimators that account for subject matter constraints and adapt to the smoothness of the underlying data. Researchers will also explore approaches for...
- This $200,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research to investigate the K-Nearest Neighbors Interference Model (KNNIM) for estimating causal effects in experimental settings with treatment interference. The project aims to derive estimators for nearest neighbor treatment effects, develop tests for relaxed KNNIM models, and create effective experimental designs for improved estimation and...
- 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...
- This award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) provides $160,000.00 to Carnegie Mellon University to develop a new game-theoretic approach to statistical inference. The key products and services to be delivered include: Developing a fundamental theory and methodology for nonparametric, game-theoretic statistical inference, including hypothesis testing, confidence intervals/sequences, and change point detection. This work aims to create more...
- This Project Grant from the National Science Foundation provides $425,000 to Carnegie Mellon University from July 1, 2021 to June 30, 2024. The funding supports research titled "Statistical Procedures and Performance Measures for Simulator-Based Frequentist Inference" under the Mathematical and Physical Sciences program (CFDA 47.049). The research aims to develop statistical methodologies and performance metrics for frequentist inference approaches that utilize simulation-based...
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 detecting and modeling interference, helping researchers draw more credible conclusions even when interference is strongly measurable. Freely available software will be developed to enable broader use of the new techniques. The award period runs from September 1, 2025 through August 31, 2027.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $155.1k | 7/31/25 |