We invite two types of submissions: 

(1) research papers presenting novel contributions to causality and decision making, and 

(2) retrospective papers that reflect on the impact, implications, and lessons learned from prior work in this area. 

 Topics of interest include, but are not limited to:

• Causal frameworks for reinforcement learning, multi-arm Bandits, and planning

• Causal representation learning for efficient decision making

• Causal discovery in sequential and interactive environments

• Counterfactual reasoning for policy evaluation and optimization

• Safe and robust decision making under uncertainty and distributional shift

• Theoretical connections between structural causal models and Markov decision processes

• Successful applications in healthcare, robotics, economics, recommender systems, and scientific discovery

We restrict the format to be up to 4 pages, excluding references and appendices, double-blind in the UAI format. All accepted papers and reviews will be publicly available via OpenReview. If applicable, we plan to have one Best Paper Award to be assigned to the paper standing out the most, after a round of discussion of the PC members and the organizers. 

Submissions should be made through the OpenReview page.

• Call for papers: April 2026

• Submission deadline: May 20th, 2026

• Notification: June 20th, 2026