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)