The center 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 selected 3 very different biomedical problems to use as test beds for our algorithms and to drive the development of new algorithms that meet the needs of biomedical researchers.
We are implementing an integrated set of methods that support the graphical representation, discovery, and application of causal knowledge from large and complex biomedical data (see samples of structural causal
The Center for Causal Discovery is working together with other BD2K Centers to promote novel methods to analyze Big Data and to explore interoperability with tools and software developed by
Center for Causal Discovery Distinguished Lecture in Causal Discovery University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University Ricardo Silva, PhD, Lecturer, Department of Statistical Science and Centre for Computational Statistics and Machine Learning, University College London, “Learning Causal Effects: Bridging Instruments and Backdoors,” at 11:00 am […]
Center for Causal Discovery Distinguished Lecture in Causal Discovery University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University Ioannis Tsamardinos, PhD, Associate Professor, Department of Computer Science, University of Crete, Greece, “Logic-Based Causal Discovery for Heterogeneous Datasets,” at 11:00 am on Friday, August 19, 2016, in Rooms […]
The 2016 CCD Summer Short Course Videos can be found on the CCD YouTube channel. Thank you to all who attended the 2016 Summer Short Course and helped make it a success. We look forward to next year’s event! Other CCD presentation videos can be found here: http://www.ccd.pitt.edu/training/presentation-videos/.