The 2017 ACM SIGKDD Workshop on Causal Discovery


August 14, 2017, Halifax, Nova Scotia, Canada


·      Workshop program is available.

·      Paper acceptance notification postponed to June 23.

·      May 26: we are pleased to announce that Prof. Ioannis Tsamardinos will deliver a keynote speech titled "Advances in Causal-Based Feature Selection" at the workshop. Abstract of the talk can be found here.

·      May 19: Clarification about page limit: there is no page limit on papers submitted to the workshop.

·      March 31: CD 2017 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 and following the success of CD 2016, CD 2017 continues to serve as 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 a special issue of Springer International Journal of Data Science and Analytics subject to further review.