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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 University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University Title: Applying graphical causal models to the study of Pseudomonas aeruginosa pathoadaptation during cystic fibrosis chronic rhinosinusitis Speaker: Catherine R. Armbruster – Postdoctoral Scholar, The Laboratory of Jennifer M. Bomberger, Department of Microbiology and […]
Center for Causal Discovery Distinguished Lecture in Causal Discovery University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University Jonas Peters, PhD, Associate Professor of Statistics, Department of Mathematical Sciences, University of Copenhagen (Denmark), “Invariance and Causality” at 11:00 am on Thursday, May 17, 2018, in Rooms […]
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 […]