The 2021 ACM SIGKDD Workshop on Causal Discovery
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Call for papers
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 2016-2020, CD 2021 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:
Important Dates
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 Journal of Machine Learning Research. All papers must be submitted via EasyChair submission system.
Workshop Organizers
Thuc Le, University of South Australia
Jiuyong Li, University of South Australia
Greg Cooper, University of Pittsburgh
Sofia Triantafyllou, University of Pittsburgh
Elias Bareinboim, Columbia University
Huan Liu, Arizona State University
Negar Kiyavash, École polytechnique fédérale de Lausanne
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 2016-2020, CD 2021 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 20, 2021: Paper submission deadline
- June 10, 2021: Notification of acceptance/rejection
- July 10, 2021: camera-ready submission deadline for accepted papers
- August 14, 2021: 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 Journal of Machine Learning Research. All papers must be submitted via EasyChair submission system.
Workshop Organizers
Thuc Le, University of South Australia
Jiuyong Li, University of South Australia
Greg Cooper, University of Pittsburgh
Sofia Triantafyllou, University of Pittsburgh
Elias Bareinboim, Columbia University
Huan Liu, Arizona State University
Negar Kiyavash, École polytechnique fédérale de Lausanne