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UAI 2017

7th Causality Workshop: Learning, Inference, and Decision-Making
Tuesday, August 15, 2017
Sydney, Australia

Introduction

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.

Invited Speakers

TBA.

Important Dates

May 26 May 19 paper submission deadline
June 16 author notification
June 17 open problem submission deadline
June 26 author notification for open problem submission
July 15 workshop paper version due
August 15 Workshop
(last day of the UAI 2017 main conference, August 11-15)

Organizers

Elias Bareinboim, Purdue (Chair)

Kun Zhang, CMU

Caroline Uhler, MIT

Jiji Zhang, Lingnan University

Dominik Janzing, MPI

Program committee

TBA.