CAIH Lab
Research
Our methodological research centres on causal and reliable AI for personalised decision-making, with healthcare as a principal application domain.
Core areas
Research themes
Causal AI
Developing AI methods that reason about causes, interventions and counterfactuals.
Agentic AI
Exploring autonomous and collaborative AI agents for research and healthcare.
Personalised Healthcare
Developing data-driven methods for patient-specific treatments and clinical decision support.
Technical breadth
Wider research scope
- Personalised decision-making and individualised treatment-effect estimation
- Causal inference and discovery from large-scale observational data
- Counterfactual reasoning for fairness, explainability and decision support
- Causal foundation models and digital twins
- Decision-making under uncertainty and conformal prediction
- Agentic and collaborative AI systems
- Multimodal, federated and continual learning
- Synthetic data, causal benchmarking and evaluation
- Robustness, domain adaptation and out-of-distribution detection
- Applications in personalised healthcare and clinical decision support