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.

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
• Integration of experimental and observational data for causal inference and causal discovery
• Algorithmic fairness in prediction and decision
• Causal explainable machine learning
• Applications of causal discovery and causal inference in healthcare, education, business, etc.
• Applications of data mining approaches to causal discovery
• Assessment of causal discovery methods

     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 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

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