This course provides an introduction to causal inference and causal representation learning, offering both theoretical foundations and hands-on training. Participants will learn how to apply these methodologies to various biomedical data types, including clinical, genotype-phenotype, molecular, and multimodal data.

Causal inference and causal representation learning are emerging fields in AI and biomedical data science, enabling a shift from associational to cause-effect reasoning. These approaches have significant applications in biomedicine, such as evaluating treatment effectiveness, understanding causal mechanisms, identifying genetic risk factors, and uncovering causal relationships in complex molecular datasets. For instance, they can help answer questions like how effective a treatment is in preventing disease, whether its effects are direct or mediated by intermediate variables, and which genetic variants causally increase disease risk and can be targeted by drugs.

Participants will gain practical experience using widely adopted tools and resources to apply causal techniques in real-world biomedical contexts. The course also features keynote talks that will provide insights into the role of causality in genomic, molecular, and computational biology, highlighting recent advancements and future directions in the field.

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Grade 2 of 3