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CD 2014 : The 2nd IEEE ICDM Workshop on Causal Discovery


When Dec 14, 2014 - Dec 14, 2014
Where Shenzhen, China
Submission Deadline Sep 26, 2014
Notification Due Oct 10, 2014
Final Version Due Oct 20, 2014
Categories    data mining   causal discovery   computer science   machine learning

Call For Papers

* Call for Papers -- CD 2014 *
* The 2nd IEEE ICDM Workshop on Causal Discovery *
* December 14, 2014, Shenzhen, China *
* Workshop website: *
* ==Paper submission deadline extended to September 26, 2014==*

Traditionally causal relationships are identified with randomised controlled experiments. However, conducting such experiments is impossible in many cases due to cost or ethical concerns. Therefore discovering causal relationships from passively observed data is considered as an alternative to controlled experiments. In the field of computer science, causal discovery from observational data has attracted enormous research efforts in the past few decades. More recently with the rapid accumulation of huge volume of data, causal discovery is seeing exciting opportunities, as well as
greater challenges.

This workshop is aimed at bringing together researchers and practitioners from different disciplines to share their research in causal discovery, and to explore the possibility of interdisciplinary collaboration in the study of causality. Based on the platform of ICDM, this workshop is especially interested in attracting contributions that link data mining research with causal discovery, and solutions to causal discovery from large scale data sets.

Topics of Interest

The workshop invites submissions on all topics of causal discovery, including but not limited to:
* Causal structure learning
* Local casual structure discovery
* Causal discovery from high-dimensional data
* Efficient causal discovery methods
* False discovery control in causal discovery
* Integrating experimental (interventional) and observational data for causal discovery
* Real-world applications of causal discovery
* Data mining approaches to causal discovery
* Assessment of causal discovery methods

Key Dates
September 26, 2014: Paper submission deadline (extended)
October 10, 2014: Notification of acceptance/rejection
October 20, 2014: Camera-ready version due for accepted papers
December 14, 2014: Workshop date

Paper Submission and Publications
Papers submitted to this workshop must not be under review or accepted for publication elsewhere. All submitted papers will be reviewed and selected by the program committee on the basis of originality, technical quality, relevance to the workshop and presentation quality.

Papers must follow the IEEE ICDM format requirement. Detailed formatting instructions will be available at All papers should be submitted via the ICDM 2014 Workshop submission system. Detailed instructions and submission link will be available on ICDM 2014 conference site: Accepted papers will be included in ICDM 2014 Workshop Proceedings published by IEEE Computer Society Press.

Workshop Organizers
Jiuyong Li, University of South Australia, Australia
Kun Zhang, Max Planck Institute for Intelligent Systems, Germany
Takashi Washio, Osaka University, Japan
Lin Liu, University of South Australia, Australia

Further Information
Please visit workshop website:

Related Resources

ICDM 2016   The IEEE International Conference on Data Mining
ICML 2017   34th International Conference on Machine Learning
PAKDD 2017   The 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining
CD 2016   The 2016 ACM SIGKDD Workshop on Causal Discovery
IJCAI 2017   International Joint Conference on Artificial Intelligence
ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
IROS 2017   IEEE/RSJ International Conference on Intelligent Robots and Systems
DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
MLDM 2017   Machine Learning and Data Mining in Pattern Recognition
JDSA Speical Issue 2016   Special issue on Causal Discovery for the Journal of Data Science and Analytics