Our research group is led by Dr. Adèle Ribeiro. Our work focuses on detecting and quantifying cause–effect relationships in complex biomedical data using AI-supported statistical methods. Our mission is to advance both the theoretical foundations and the translational research of explainable AI and causal inference, enabling deeper scientific insight and more effective data-driven decision-making in the life and health sciences. We aim to understand why biological and health systems function the way they do and how targeted interventions can influence their behavior.

Real-world biomedical datasets rarely meet standard methodological assumptions: they are often high-dimensional, heterogeneous, and multimodal, and may be affected by latent confounding, selection bias, privacy constraints, and limited sample sizes. If these challenges are not carefully addressed, causal analyses risk producing invalid, non-reproducible, or non-generalizable results.

Our research addresses these challenges by developing methods that are both rigorous and effective in real-world settings, with a focus on:

Robust Causal Learning
Learning reliable causal relationships despite hidden confounders and limited data

Uncertainty Quantification
Providing statistically valid and trustworthy conclusions with uncertainty estimates

Expert Knowledge Integration
Incorporating expert knowledge while accounting for uncertainty and conflicting information

Collaborative Analysis
Enabling research collaborations across institutions without sharing sensitive data

High-Dimensional Scaling
Efficiently processing complex, multimodal datasets at scale

Our ongoing applications include public health and clinical research on malaria, mental health, cardiovascular diseases, post-acute infection syndromes including long COVID, and cancer. In all these areas, causal insights have the potential to improve understanding and to support targeted strategies for prevention, diagnosis, and treatment.

By tackling core challenges in causal inference—such as reliability, heterogeneity, privacy, knowledge integration, and scalability—our research contributes to the development of explainable, trustworthy, and actionable AI for biomedical discovery and precision health.

Contact: Dr. Adèle Ribeiro
 

Funding reference number: 01ZU2503