Current workshops
Overview:
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 2021, CD 2022 continues to serve as a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery. 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.
Papers accepted by the workshop are to be published in Proceedings of Machine Learning Research (and presented on the workshop day).
Previous workshops
- The 2021 ACM SIGKDD Workshop on Causal Discovery
- The 2020 ACM SIGKDD Workshop on Causal Discovery
- The 2019 ACM SIGKDD Workshop on Causal Discovery
- The 2018 ACM SIGKDD Workshop on Causal Discovery
- The 2017 ACM SIGKDD Workshop on Causal Discovery
- The 2016 ACM SIGKDD Workshop on Causal Discovery
- IEEE ICDM Workshop on Causal Discovery 2013