Causal Inference: Learning and Prediction
Sunday July 27th, 2014
Quebec City, Quebec, Canada
Causal Inference: Learning and Prediction is a workshop that took place immediately after the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014).
Introduction
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, both for causal structure learning and for making causal predictions (i.e., predicting what happens under interventions). 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 inference from observational and interventional data, especially when latent (confounding) variables or feedback loops may be present. Contributions describing practical applications of causal methods are specially encouraged. This one-day workshop will explore these topics through a set of invited talks, presentations and a poster session.
This workshop follows on from a successful predecessor at UAI 2013.
Invited Speakers
We are very pleased to announce our keynote speakers for the workshop:
Robert Spekkens, Perimeter Institute for Theoretical Physics
Elias Bareinboim, University of California
Important Dates
Organisers
Joris Mooij, University of Amsterdam (Chair)
Dominik Janzing, Max Planck Institute for Intelligent Systems
Jonas Peters, ETH Zürich
Tom Claassen, Radboud University Nijmegen
Antti Hyttinen, California Institute of Technology
Program committee
We thank our reviewers: Thomas Richardson, Ricardo Silva, Markus Kalisch, Frederick Eberhardt, Alain Hauser, Ilya Shpitser, Robin Evans, Kun Zhang, Eleni Sgouritsa, Aapo Hyvaerinen, Jan Lemeire, James Robins, Chris Meek, Preetam Nandy, Philipp Geiger, Nicholas Cornia, Oliver Stegle.