This $125,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research on advancing the theory and practice of causal learning using machine learning methods. The project aims to develop new approaches for imputing unobserved counterfactual outcomes, quantifying uncertainty in treatment effect estimation, and establishing a statistical framework for finite-population inference. The work will...
This $462,500 Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070), supports the development of advanced causal inference methods for data-driven decision making. Key products and services to be delivered include: Automated and robust causal AI systems that integrate machine learning and causal inference techniques to enable more decision-makers to leverage causal analysis. The project will...
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 $616,000 Project Grant from the National Science Foundation (NSF) Division of Computing and Communication Foundations, under the CFDA program "Computer and Information Science and Engineering", supports research at the California Institute of Technology (Caltech) to address algorithmic and information-theoretic challenges in causal inference. The key objectives are to: Increase the range of applicability of causal inference methods by developing new algorithms and sample...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $329,020.00 to the Regents of the University of Michigan to develop new analytical tools for making informed decisions based on complex, high-dimensional unstructured data such as text, images, and gene expressions. The project aims to enhance current methods in machine learning and causal inference, enabling more reliable and...
This $150,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of foundational principles, algorithms, and tools for causal decision-making systems. Researchers at Columbia University will enrich traditional artificial intelligence formalism with causal modeling to enable more efficient, robust, and explainable decision-making by autonomous systems. Key deliverables include integrating...
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...
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 $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 Project Grant award of $250,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research led by Carnegie Mellon University to develop flexible and valid inference procedures for modern complex data that leverage powerful black-box machine learning algorithms. The key focus is on advancing cross-validation techniques to enable adaptive inference in conjunction with these opaque models, with potential applications in areas such as...