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:
September 30October 12 - Reviews release: October 31
- Final papers submission: November 17
- Conference date: December 8 – 9
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 Lecture Notes in Computer Science (https://www.springer.com/gp/computer-science/lncs/new-latex-templates-available/15634678)
Papes should be sent using the following link
The presentation format will be hybrid, either in-person or virtual, according to the authors’ preferences.
Authors of the best papers will be invited to submit extended versions to “Causal Graphical Models and Their Applications 2nd Edition”, a special issue of Entropy (Link).
CaDis 