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
The Center for Causal Discovery has released the newest version of its causal discovery software, Tetrad (Version 6.4) and causal command command-line program (Version 0.3). The focus on this release for Tetrad has been adding additional algorithms, an algorithm chooser, bootstrap for estimating edge probabilities to all algorithms. There are […]
Center for Causal Discovery Distinguished Lecture in Causal Discovery University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University Joris M. Mooij, PhD, Associate Professor, Informatics Institute, University of Amsterdam (the Netherlands), “Validating Causal Discovery Methods” at 11:00 am on Thursday, April 19, 2018, in Rooms 407A/B BAUM, […]
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, […]