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 David Jensen, DSc, Professor, College of Information and Computer Sciences, University of Massachusetts Amherst, “The Case for Empirical Evaluation of Methods for Causal Modeling” at 11:00 am on Thursday, […]
Center for Causal Discovery Distinguished Lecture in Causal Discovery University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University Jonas Almeida, PhD, Professor and Chief Technology Officer, Department of Biomedical Informatics, Stony Brook University (SUNY), “Data Science for Biomedical Informatics in the Planet of the Apps” at 11:00 […]
Center for Causal Discovery Datathon Winners for 2017 The Center for Causal Discovery (CCD) held the second annual datathon at the end of the 2017 Summer Short Course on Causal Discovery at Carnegie Mellon University, Pittsburgh, Pennsylvania. The five day course from June 12 to 16 included three and a […]