Causal AI Methods for Computational Biology

Translate biological hypotheses into causal questions using potential outcomes and directed graphs. The practical programme identifies confounding, mediation and collider bias and implements matching, propensity scores, inverse weighting, g-computation and doubly robust estimation. It then develops causal machine-learning approaches, including forests, targeted learning, uncertainty quantification and conformal prediction, before applying mediation analysis to multi-omics integration and reproducible R/Python workflows.

Sessions: 2–6 November, 14:00–18:00 daily, Europe/Berlin (CET, UTC+01:00), with Slack discussion between sessions.

Live online training; fee €480. For advanced MSc, doctoral and postdoctoral researchers with R/Bioconductor, probability, statistics and regression experience.