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 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 […]
Center for Causal Discovery Distinguished Lecture in Causal Discovery University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University ChengXiang Zhai, PhD, Professor and Willett Faculty Scholar, Department of Computer Science, University of Illinois, “Statistical Approaches to Analysis of Traditional Chinese Medicine Practice Records” at 11:00 am on […]
Center for Causal Discovery Distinguished Lecture in Causal Discovery University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University Thomas Richardson, PhD, Professor and Chair, Department of Statistics, University of Washington, “Nested Markov Models” at 11:00 am on Thursday, April 20, 2017, in Rooms 407A/B BAUM, 5607 Baum […]