EMBO Practical Course | Causality in Biomedicine: Going Beyond Associations 2026

EMBO Practical Course | Causality in Biomedicine: Going Beyond Associations 2026 is organized by European Molecular Biology Organization (EMBO), European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) and will be held from Oct 04 - 09, 2026 at European Bioinformatics Institute (EMBL-EBI), Cambridge, England, United Kingdom.

Description:
This course provides an introduction to causal inference and causal representation learning, offering both theoretical foundations and hands-on training. You 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-and-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.

Gain practical experience using widely adopted tools and resources to apply causal techniques in real-world biomedical contexts. This EMBO Practical Course will also feature 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.

Learning outcomes:
After the course, you should be able to:

• Assess the difference between causal and associational estimation 
• Explain the difference between randomised experiments vs observational studies in the context of public health and the human ecosystem
• Perform nonparametric estimation techniques on real-world health data to estimate causal effects on biomedical data
• Examine challenges of causal discovery for high-throughput molecular studies
• Assess whether causality can be extracted from data and, if so, apply the appropriate technique to extract causality in experimental and observational settings
• Apply causal representation learning to extract causal (latent) features from molecular/imaging data

Course content:
During this course, you will learn about: 

• Causal inference and applications to clinical and genotype-phenotype data
  • Estimating causal effects: why correlations alone are misleading
  • Data in causality: randomised trials vs observational data
  • Estimation techniques for causal effects, e.g., targeted learning
  • Applications in simple clinical settings 
• Causal representation learning and application to multimodal molecular data
  • Extracting causal structure from observations: assumptions and challenges
  • Multimodal learning for causal (latent) feature discovery
  • Application of these methods to multimodal molecular data
  • Synergies of causal inference and representation learning in biomedicine