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CDPD 2023 : The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision

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Link: http://4llab.net/workshops/CDPD2023/index.html
 
When Aug 7, 2023 - Aug 7, 2023
Where Long Beach, CA, USA
Submission Deadline Jun 1, 2023
Notification Due Jun 26, 2023
Final Version Due Jul 8, 2023
Categories    causality   data mining   machine learning   statistics
 

Call For Papers

**************************************************************
** Call for Papers **
**The 2023 ACM SIGKDD Workshop on Causal Discovery, **
** Prediction and Decision (CDPD 2023). **
** August 07, 2023, Long Beach, CA, USA **
** Held in conjunction with KDD'23 **
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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.

CDPD-2023 serves 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 discovery
• Causal inference
• Causal decision making
• Causality-inspired prediction
• Stable learning and OOD generalization
• Causal structure learning
• Integration of experimental and observational data for causal inference and causal discovery
• Algorithmic fairness in prediction and decision
• Causal explainable machine learning
• Causal applications in healthcare, education, business, etc.

** Important Dates
- June 01 (extended), 2023: Paper submission deadline
- June 23, 2023: Notification of acceptance/rejection
- July 08, 2023: Camera-ready submission deadline for accepted papers
- August 07, 2023: 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=cdpd2023). Within the submission system, please choose "CDPD 2023” for your submission.


** Workshop Organizers
• Thuc Le, University of South Australia
• Jiuyong Li, University of South Australia
• Robert Ness, Microsoft Research, USA
• Sofia Triantafyllou, University of Crete, Greece
• Shohei Shimizu, Shiga University & RIKEN, Japan
• Peng Cui, Tsinghua University, China
• Kun Kuang, Zhejiang University, China
• Jian Pei, Duke University, USA
• Fei Wang, Cornell University, USA
• Mattia Prosperi, University of Florida, USA




** Further Information
Please visit workshop website: http://4llab.net/workshops/CDPD2023/index.html

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