We invite contributions relevant to any aspect of causal discovery, including theoretical and practical aspects, and applications. The submissions can be in any of the following topics, but not limited to:
- Causal discovery from observational data
- Causal discovery from interventions
- Hybrid causal discovery
- Building causal models from human expert knowledge
- Causal discovery from time series
- Reinforcement learning and causal discovery
- Deep learning and causal discovery
- Benchmarks for causal discovery
- Applications to real-world problems
Important dates:
- Paper submission deadline: August 10
- Reviews release: September 15
- Final papers submission: September 20
- Conference date: November 30 – December 1
Submissions:
We accept two types of submissions: (i) normal papers that present a relevant contribution (limited to 12 pages), (ii) extended abstracts for preliminary work or proof of concept research (limited to 4 pages). Both should be formatted according to Springer’s Communications in Computer and Information Science (https://link.springer.com/series/558/information-for-authors-and-editors)
Papers should be sent using the following link.
The presentation format will be hybrid, either in-person or virtual, according to the authors’ preferences.
The Workshop’s Proceedings will be published by Springer in the Communications in Computer and Information Science (CCIS) series.
CaDis 