The symposium will explore topics related to, but not limited to:

• Causal discovery;
• Causal learning and control problems;
• Theoretical Foundations of Causal Inference;
• Causal Inference and Active Learning;
• Causal learning under low data;
• Reinforcement learning;
• Causal machine learning;
• Causal Generative Models;
• Benchmark for causal discovery and causal reasoning.

The symposium will feature several keynote speakers, including Antoine Chambaz, Eric Gaussier, Yingzhen Li, Elina Robeva, Chandler Squires, Bin Yu, among others.

In addition, we invite poster sessions on all aspects of causality.

We allow abstract submissions. All submissions must be in English, in PDF format, and in two-column ACM format. Appropriate LaTeX and Word templates are available on the ACM website: https://www.acm.org/publications/proceedings-template.

Authors should submit their abstracts via Easy Chair. Please note that authors are encouraged to adhere to the best practices of Reproducible Research (RR), by making available the data and software tools to reproduce the results presented in their papers. For reasons of persistence and proper attribution of authorship, we request the use of standard repository hosting services such as dataverse, mldata, openml, etc. for datasets, and mloss, bitbucket, github, etc. for source code.

Important dates

• Deadline for submissions: February 9, 2023 (23:59 AOE)
• Notification to authors: February 28, 2023
• Conference: April 17-21, 2023