Advances in Causal Inference
Advances in Causal Inference was a workshop that took place immediately after the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015).
Thursday July 16th, 2015
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. For this year, we encourage submissions on causal inference for less standard data and generative models, such as point processes, relational structures and social networks, but all areas in causal learning are welcome. 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 2014.
|15 May 2015|
|12 June 2015|
|12 July 2014|
|16 July 2015|
|16 August 2015|
Ricardo Silva, University College London (Chair)
Tom Claassen, Radboud University Nijmegen
Robin Evans, University of Oxford
Jonas Peters, Max Planck Institute for Intelligent Systems
Ilya Shpitser, University of Southampton