This National Science Foundation Project Grant of $233,835 supports research at Purdue University from January 2023 through December 2027 under the Computer and Information Science and Engineering program (CFDA 47.070). The research aims to develop novel algorithms using information-theoretic methods to expand the scope of causal reasoning from data. Specifically, the grantee will pursue three thrusts: first, using an information-theoretic interpretation of Occam's Razor based on causal...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $462,500 to The Leland Stanford Junior University to advance the state-of-the-art in causal artificial intelligence (AI) methods. The project aims to automate key components of the causal analysis pipeline and develop robust data-driven estimation procedures to enable more decision-makers to leverage causal AI systems. The...
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...
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 $130,291 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to Cornell University on July 1, 2024. The grant supports the development of new methods for causal inference that address interference arising from network interactions and time dynamics. This research aims to enhance randomized experimentation techniques used across fields like natural and social sciences, engineering,...
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...
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 $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 a $300,902 Project Grant to Texas A&M University under the Mathematical and Physical Sciences program (CFDA 47.049) for research titled "Automated Causal Discovery with Observational Data via Directed Graphical Models - New Theory and Methods." The three-year project beginning July 1, 2021 will develop new statistical theory and computational methods for discovering causal relationships from observational data using directed graphical...
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...