2-6 November 2026

This course introduces causal machine learning and artificial intelligence (AI) methods for computational biology. Over five half-days, participants will learn the foundational frameworks for defining and estimating causal effects, including potential outcomes and directed acyclic graphs (DAGs), and progress from classical estimation methods to modern AI/ML approaches. Throughout, biological datasets serve as motivating examples for hands-on exercises using publicly available tools and resources.

This course is designed for advanced MSc, PhD, and postdoctoral researchers in statistics, biostatistics, and computational biology. Working knowledge of R/Bioconductor is required. Familiarity with probability and statistics and some experience with linear and logistic regression are also expected. Prior exposure to machine learning is helpful but not required.

By the end of this course, participants will be able to:
•    translate scientific hypotheses into causal queries and formulate causal questions 
•    construct and interpret DAGs to identify confounders, mediators, and colliders
•    implement classical causal estimation methods including propensity score matching, inverse probability weighting, g-computation, and doubly robust estimation
•    apply AI and machine learning methods for causal inference, including causal forests and targeted learning
•    integrate causal reasoning into multi-omics and computational biology workflows
•    build reproducible causal analysis pipelines using R and Python packages

Live sessions will be held daily from 14:00 to 18:00 CET. Between sessions, participants can engage with instructors and peers via Slack (platform details provided before the course).

MONDAY, NOVEMBER 2
•    14:00–14:15: Welcome and practicalities
•    14:15–14:45: Why causal inference? From association to causation in biology
•    14:45–15:15: The potential outcomes framework: counterfactuals, ATE, ATT
•    15:15–16:00: SUTVA and causal graphs 
•    16:00–16:30: Break
•    16:30–17:30: Hands-on – causal diagrams 
•    17:30–18:00: Q&A and discussion

TUESDAY, NOVEMBER 3

•    14:00–14:15: Recap
•    14:15–14:45: Propensity scores
•    14:45–15:15: IPW
•    15:15–16:00: G-computation and doubly robust estimation
•    16:00–16:30: Break
•    16:30–17:30: Hands-on - causal estimation 
•    17:30–18:00: Q&A and discussion

WEDNESDAY, NOVEMBER 4

•    14:00–14:15: Recap
•    14:15–14:45: DAGs
•    14:45–15:15: Sufficient adjustment set and backdoor adjustment
•    15:15–16:00: Matching
•    16:00–16:30: Break
•    16:30–17:30: Hands-on - DAGs
•    17:30–18:00: Q&A and discussion

THURSDAY, NOVEMBER 5

•    14:00–14:15: Recap
•    14:15–14:45: Causal machine learning and causal AI 
•    14:45–15:15: Quantifying uncertainty in causal AI models
•    15:15–15:45: Break
•    15:45–16:45: Hands-on - causal machine learning 
•    16:45–17:30: Hands-on - causal conformal prediction 
•    17:30–18:00: Q&A and discussion 

FRIDAY, NOVEMBER 6
•    14:00–14:15: Recap
•    14:15–14:45: Causal mediation analysis in computational biology
•    14:45–15:30: Causal reasoning in multi-omics integration
•    15:30–16:00: Break
•    16:00–17:30: Hands-on - multimodal causal mediation analysis 
•    17:30–18:00: Final Q&A and course wrap-up

Machine Learning with R - ONLINE, 16-20 February

AI for Genomics - ONLINE, 7-9 April

Metatranscriptomics - ONLINE, 27-29 April

Machine Learning for Multi-Omics Integration - ONLINE, 21-23 September

Machine Learning for Drug Discovery - ONLINE, 12-15 October