UAI 2017 Causality Workshop
Dear friends in causality research,
We would like to promote an upcoming causality workshop at UAI 2017. See the details below for more information:
Causality in Learning, Inference, and Decision-making: Causality shapes how we view, understand, and react to the world around us. It’s a key ingredient in building AI systems that are autonomous and can act efficiently in complex and uncertain environments. It’s also important to the process of scientific discovery since it underpins how explanations are constructed and the scientific method.
Not surprisingly, the tasks of learning and reasoning with causal-effect relationships have attracted great interest in the artificial intelligence and machine learning communities. This effort has led to a very general theoretical and algorithmic understanding of what causality means and under what conditions it can be inferred. These results have started to percolate through more applied fields that generate the bulk of the data currently available, ranging from genetics to medicine, from psychology to economics.
This one-day workshop will explore causal inference in a broad sense through a set of invited talks, open problems sessions, presentations, and a poster session. In this workshop, we will focus on the foundational side of causality on the one hand, and challenges presented by practical applications on the other. By and large, we welcome contributions from all areas relating to the study of causality.
We encourage co-submission of (full) papers that have been submitted to the main UAI 2017 conference. This workshop is a sequel to a successful predecessor at UAI 2016.
Dates/Locations: August 15, 2017; Sydney, Australia.
Speakers: TBA
Registration and additional information: https://www.cs.purdue.edu/homes/eb/causal-uai17/