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 $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...
This $574,140 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of novel algorithms to extract causal relationships from diverse, unstructured datasets. The project aims to address limitations of current causal discovery methods that rely heavily on interventional data, by creating algorithms that can leverage common causal knowledge across datasets from different environments....
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 $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 $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) Office of Advanced Cyberinfrastructure awarded a $599,905 Project Grant to Arizona State University, Division Orspa, to develop CAUSALBENCH, a cyberinfrastructure for causal-learning benchmarking. The grant was awarded on July 1, 2023 under the NSF Computer and Information Science and Engineering (CISE) program (CFDA 47.070), which funds research and education in all areas of computing, communications, and information science. The key products and services...
This Project Grant award, funded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), supports collaborative research to advance the theory and practice of causal learning using modern machine learning tools. The $125,000 award to the University of California, Berkeley aims to: (1) develop methods for imputing unobserved counterfactual outcomes by integrating flexible machine learning models with statistical principles; (2) promote design-based...
This three-year, $600,000 Project Grant from the National Science Foundation's Division of Computer and Network Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of causal reasoning techniques to optimize network performance and answer "what-if" questions about potential network design changes using passively collected data. The grant recipient, Purdue University, will develop causal dependency graphs for...
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...