Workshop in Bellevue, WA
Approches to Causal Struture Learning was a workshop which took place immediately after the 29th Conference on Uncertainty in Artificial Intelligence (UAI). Feel free to browse the website and see the submitted papers and speakers' slides. Full details about UAI 2013 can be found here.
Monday July 15th, 2013
We're very please to announce our plenary lecture speakers for the workshop:
David Heckerman, Microsoft Research
Joris Mooij, Radboud University Nijmegen
Causality is central to how we view and react to the world around us, to our decision making, and to the advancement of science. Causal inference in statistics and machine learning has advanced rapidly in the last 20 years, leading to a plethora of new methods. However, a side-effect of the increased sophistication of these approaches is that they have grown apart, rather than together.
The aim of this workshop is to bring together researchers interested in the challenges of causal structure learning from observational and experimental data especially when latent or confounding variables may be present. Topics related to causal structure learning will be explored through a set of invited talks, presentations and a poster session.
This workshop follows on from a successful predecessor at UAI 2012.
Robin Evans, University of Cambridge (Chair)
Marloes Maathuis, ETH Zurich
Thomas Richardson, University of Washington
Ilya Shpitser, University of Southampton
Jin Tian, Iowa State University