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CD 2017 : The 2017 ACM SIGKDD Workshop on Causal Discovery


When Aug 14, 2017 - Aug 14, 2017
Where Halifax, Nova Scotia, Canada
Submission Deadline May 26, 2017
Notification Due Jun 16, 2017
Final Version Due Jul 31, 2017
Categories    data mining   machine learning   computer science

Call For Papers

** Call for Papers **
**The 2017 ACM SIGKDD Workshop on Causal Discovery (CD 2017)**
** August 14, 2017, Halifax, Nova Scotia, Canada **
** Held in conjunction with KDD'17 **

***Accepted workshop papers are to be published in a Special Issue of Springer International Journal of Data Science and Analytics subject to further review***

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.

** 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 26, 2017: Paper submission deadline
- June 16, 2017: Notification of acceptance/rejection
- July 31, 2017: Camera-ready submission deadline for accepted papers
- August 14, 2017: 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 Instructions for Authors of the Springer International Journal of Data Science and Analytics (JDSA). All papers must be submitted via JDSA submission system ( Within the submission system, please choose “S.I.: Causal Discovery 2017” for your submission. Camera-ready version of all accepted workshop papers will be invited to undergo further review by JDSA, and papers accepted after the further review will be included in the Special Issue on Causal Discovery 2017 of JDSA to be published in early 2018.

** Workshop Organizers
Lin Liu, University of South Australia
Jiuyong Li, University of South Australia
Kun Zhang, Carnegie Mellon University
Emre Kiciman, Microsoft Research
Negar Kiyavash, University of Illinois at Urbana-Champaign

** Further Information
Please visit workshop website:

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