Project Grant 2617859
- The National Science Foundation Division of Computing and Communication Foundations awarded The Johns Hopkins University $206,849 under the Computer and Information Science and Engineering program (CFDA 47.070) on October 1, 2025, for research on causal reasoning using information-theoretic methods. The project runs through December 31, 2027, with place of performance in Baltimore, Maryland. The research develops algorithms that identify information-theoretically simple explanations of causal...
- The National Science Foundation Division of Information and Intelligent Systems awarded a $399,923 project grant to The Johns Hopkins University from September 2021 through August 2024. This funding supports research titled "CAUSAL AND SEMI-PARAMETRIC INFERENCE FOR EXPLANATIONS OF DISPARITIES AND DISPARITY-CORRECTING MODELING" under the NSF's Computer and Information Science and Engineering program (CFDA 47.070). The goal of this project grant is to advance the development of causal...
- 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...
- The National Science Foundation Division of Mathematical Sciences awarded Cornell University $303,663 on July 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop theory and methods for causal discovery in empirical sciences and machine learning. The project, a five-year effort ending June 30, 2031, will establish rigorous approaches to reasoning about uncertainty in estimated causal structure and enable practitioners to apply causal discovery methods reliably...
- 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...
- The National Science Foundation Division of Computing and Communication Foundations awarded The Johns Hopkins University $357,069 on August 15, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop interpretable artificial intelligence systems that help researchers identify cross-cutting connections across the scientific literature and accelerate discovery. The project creates large language model-based frameworks designed to recognize structural...
- 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 $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 University of Chicago received a $545,359 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective August 1, 2025, through July 31, 2028. This research initiative addresses critical limitations in generative artificial intelligence (AI) systems by developing causal concept models that enable robust causal reasoning and concept discovery. The...
- The National Science Foundation Division of Computing and Communication Foundations awarded Case Western Reserve University $420,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop methods for understanding and leveraging causal knowledge within foundation models. The project addresses the gap between pattern recognition in large AI systems and true causal reasoning, which is critical for reliable deployment in healthcare,...
The National Science Foundation Division of Information and Intelligent Systems awarded The Johns Hopkins University $447,317 on October 1, 2025, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop novel algorithms for extracting causal relationships from diverse, unstructured datasets. The research addresses a gap in machine learning where existing causal discovery methods rely heavily on interventional data from controlled trials, limiting their applicability to real-world datasets from multiple environments. The project characterizes the fundamental limits of causal knowledge extraction under minimal assumptions about data-generating processes, develops causal discovery algorithms to achieve these limits, and evaluates the proposed algorithms. Outcomes are expected to unlock causal reasoning for data-rich domains and expand adoption of causal discovery among machine learning practitioners, with applications spanning medicine to computer software security. Performance occurs in Baltimore, Maryland through July 31, 2027. This is a Project Grant, the standard NSF mechanism for investigator-initiated research.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $447.3k | 2/19/26 |