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...
The National Science Foundation (NSF) awarded a $500,000 Project Grant under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program to The Trustees of Columbia University in the City of New York, doing business as Columbia University. The 3-year research project, beginning September 1, 2024, will develop and investigate new methods for estimating causal effects in complex randomized experiments. The project aims to create a general framework and statistical theory to support the...
The National Science Foundation Division of Mathematical Sciences awarded $250,000 under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to Stanford University from September 1, 2022 to August 31, 2025. The project grant funding will support research aimed at advancing statistical methods to measure replicability of scientific findings across multiple environments and populations. Specifically, the grantee will develop new approaches leveraging techniques such as...
This Project Grant award, totaling $125,000.00, was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award supports a collaborative research project aimed at developing theoretical foundations and practical methods for deriving valid, reliable, and interpretable causal insights from complex data using modern machine learning tools. The key objectives of the project are: (1) to integrate flexible machine...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $225,000 Project Grant to Carnegie Mellon University to develop a methodology for simulation-based inference that uses random features rather than carefully designed summary statistics. The 3-year grant, which runs from August 15, 2023 to July 31, 2026, aims to create a practical and generic tool for fitting simulation models to real-world data across diverse domains like astronomy, ecology, climate science, and...
This $169,999 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at the University of California, Davis (UC Davis) to advance innovative nonparametric data analysis techniques. The project aims to conduct comprehensive statistical and computational analyses to push the boundaries of modern nonparametric statistical inference, with potential applications in areas like nonparametric latent...
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...
The National Science Foundation (NSF) awarded a $249,989 project grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of New Hampshire's (UNH) Office of Sponsored Research. The grant supports the development of efficient and robust statistical tools for modeling data with measurement errors, a common challenge in fields like epidemiology and economics. The research aims to improve estimation accuracy and hypothesis testing power across linear, nonlinear, and...
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 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...