· July 19, 2016: Workshop program is available now.
· May 10, 2016: Paper submission deadline has been extended to May 23, 2016.
· March 29, 2016: Please note that the workshop date is August 14, 2016
· March 20, 2016: CD 2016 website is up
As a basic and effective tool for explanation, prediction and decision making, causal relationships have been utilized in almost all disciplines. Traditionally, causal relationships are identified by making use of interventions or randomized controlled experiments. However, conducting such experiments is often expensive or even impossible due to cost or ethical concerns. Therefore there has been an increasing interest in discovering causal relationships based on observational data, and in the past few decades, significant contributions have been made to this field by computer scientists.
Inspired by such achievements, this workshop aims to provide a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. Based on the platform of KDD, this workshop is especially interested in attracting contributions that link data mining/machine learning research with causal discovery, and solutions to causal discovery in large scale data sets.
Papers accepted by the workshop are to be published in the
Special Issue on Causal Discovery of Springer International Journal of Data
Science and Analytics subject to further review.