We are pleased to announce the upcoming online course “Causal AI Methods for Computational Biology”, taking place 2–6 November 2026.

The course will provide an introduction to causal inference and its application to computational biology, progressing from classical causal inference methods to modern machine learning and AI approaches.

Topics will include:

• Potential outcomes and causal estimands
• Directed acyclic graphs (DAGs), confounding, mediation, and colliders
• Propensity scores, inverse probability weighting, g-computation, and doubly robust estimation
• Matching and causal effect estimation
• Causal machine learning, including causal forests and targeted learning
• Uncertainty quantification and causal conformal prediction
• Causal mediation analysis
• Causal reasoning for multi-omics and multimodal data

The course includes hands-on practical sessions using biological datasets and publicly available computational tools, as well as opportunities for discussion with instructors and participants.

The course is intended for advanced MSc, PhD, and postdoctoral researchers in computational biology, biostatistics, statistics, and related fields.

Prerequisites: Working knowledge of R/Bioconductor and familiarity with probability, statistics, and linear/logistic regression. Previous machine-learning experience is helpful but not required.

Registration and course information: https://www.physalia-courses.org/courses-workshops/causal-ai-methods/

We would be grateful if you could share this announcement with colleagues and researchers who may be interested.