Center for Causal Discovery
- Develop highly efficient causal discovery algorithms that can be practically applied to very large biomedical datasets
- Conduct projects addressing 3 distinct biomedical questions (cancer driver mutations, lung fibrosis, brain causome) as a vehicle for algorithm development and optimization
- Disseminate causal discovery algorithms, software, and tools
- Train data scientists and biomedical investigators in the use of CCD tools
- Train data scientists and biomedical investigators to collaborate in the discovery of causality
Led by Drs. Gregory Cooper, Ivet Bahar, Jeremy Berg, and Clark Glymour (see figure below), the Center represents a partnership among data scientists from the University of Pittsburgh (Pitt), Carnegie Mellon University (CMU), and the Pittsburgh Supercomputing Center (PSC) who will develop the algorithms, software, and system architecture needed by biomedical scientists seeking to discover and represent causality using their large and diverse data sets. We are joined by collaborators from Yale University, California Institute of Technology, Rutgers University, Stanford University, the University of Crete, and the University of North Carolina. We receive guidance and insight from an exceptional External Advisory Board.
Scientists are invited to explore CCD tutorials and projects both within the Center and with other BD2K Consortium members to find what is needed to start discovering new causal knowledge in their own data. See http://www.ccd.pitt.edu/ for more information.