This Workshop aims to bring together researchers and professionals interested in the intersection of Bayesian statistics and causal analysis. The goal is to explore how to quantify uncertainty in the estimation of causal effects and improve the robustness of AI systems.

This Workshop will provide an informal environment for discussing:

Bayesian Causal Inference represents the cutting edge in data-driven decision-making. Unlike traditional machine learning approaches that focus on statistical associations 𝑃 (𝑌 ∣ 𝑋), this approach allows for the modeling of interventions through the do(𝑋) operator, answering questions about what would happen if we intervene in the system.

The competitive advantage of this technical framework lies in three pillars:

• Treatment of Uncertainty: In public policy or healthcare contexts, a point estimate is not enough. The Bayesian approach provides a full probability distribution for causal effects, allowing for robust risk assessment.
• Incorporation of Prior Knowledge: Allows for the integration of expert judgment and prior studies through the use of informative priors, which is vital for scenarios with limited observational data.
• Counterfactual Modeling: Enables the construction of models that can imagine scenarios that never occurred, allowing for extreme personalization in recommendation systems and precision medicine.

• Learning causal structures through MCMC.
• Counterfactual inference with Gaussian processes.
• Bayesian Deep Learning.
• Industrial applications of causal models.

• Articles must be formatted according to the IEEE-CS proceedings format. The article should be between 4 and 8 pages long including figures and references. The IEEE format is available at: http://www.ieee.org/conferences_events/conferences/publishing/templates.html.
• Submissions must be made through the Microsoft's Conference Management Toolkit.

• Paper Submission Deadline: August 3rd, 2026
• Notification of Acceptance: August 17th, 2026
• Final Version (Camera-Ready): August 24th, 2026