Full papers
Original research contributions with comprehensive experiments and analysis.
Machine Learning/CORE Unranked
CAO 2026
Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop
The CAO 2026 workshop, affiliated with ICLR 2026, focuses on advancing research in causal representation learning and its applications in artificial intelligence. It brings together researchers to present and discuss novel methods for learning causal structures from data, with an emphasis on interpretability, robustness, and real-world impact.
Key deadlines
Verified September 9, 2026Full paper
February 11, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
February 11, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Preliminary results, novel ideas, or work in progress with clear potential.
Poster presentations
Opportunities to present ongoing or completed work in a poster format.
Research areas in scope
01
Workshop Topics
Causal representation learningCausal inference from observational dataCausal discovery in high-dimensional settingsCausal models for reinforcement learningCausal generalization and out-of-distribution robustnessCausal interpretability and explainability in AICausal foundations of fairness and ethics in machine learningApplications of causal learning in healthcare, robotics, and social sciences
Policies worth checking twice
- Submissions must be anonymized for double-blind review.
- Papers must not exceed 8 pages (excluding references).
- Dual submission to other conferences or journals is not permitted during review.
- Accepted papers will be presented at the workshop and made available online.
- Authors must include a statement on the use of AI tools in the preparation of the paper.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026