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CD 2018 : The 2018 ACM SIGKDD Workshop on Causal Discovery

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Link: http://nugget.unisa.edu.au/CD2018/
 
When Aug 20, 2018 - Aug 20, 2018
Where London
Submission Deadline May 18, 2018
Notification Due Jun 8, 2018
Final Version Due Jul 20, 2018
Categories    causal discovery   data mining   machine learning
 

Call For Papers

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** Call for Papers **
**The 2018 ACM SIGKDD Workshop on Causal Discovery (CD 2018)**
** August 20, 2018, London, UK **
** Held in conjunction with KDD'18 **
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***Accepted workshop papers are to be published in Proceedings of Machine Learning Research***

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 and CD 2017, CD 2018 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.

** 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 in high-dimensional data
- Integration of experimental and observational data for causal discovery
- Real world applications of causal discovery (e.g. in bioinformatics)
- Applications of data mining approaches to causal discovery
- Assessment of causal discovery methods

** Important Dates
- May 18, 2018: Paper submission deadline
- June 08, 2018: Notification of acceptance/rejection
- July 20, 2018: Camera-ready submission deadline for accepted papers
- August 20, 2018: 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 Instruction for Authors of the Journal of Machine Learning Research (http://www.jmlr.org/author-info.html). All papers must be submitted via the EasyChair System (https://easychair.org/conferences/?conf=cd2018). Within the submission system, please choose “CD 2018” for your submission.


** Workshop Organizers
Thuc Le, University of South Australia
Kun Zhang, Carnegie Mellon University
Emre Kiciman, Microsoft Research
Aapo Hyvärinen, University College London
Lin Liu, University of South Australia


** Further Information
Please visit workshop website: http://nugget.unisa.edu.au/CD2018/

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ECML-PKDD 2018   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
ICDMML 2019   2019 International Conference on Data Mining and Machine Learning
TIST Special Issue 2018   ACM TIST Special Issue on Advances in Causal Discovery and Inference
ICCV 2019   International Conference on Computer Vision
Ei ICEPOE 2019   2019 3rd International Conference on Engineering Physics and Optoelectronic Engineering (ICEPOE 2019)
ICLR 2019   International Conference for Learning Representations
CausalML 2018   ICML / IJCAI / AAMAS Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action
IJSCMC 2018   International Journal of Soft Computing, Mathematics and Control
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