Causal AI
Developing AI methods that reason about causes, interventions and counterfactuals.
Causal AI · Personalised Decisions · Healthcare
From causal insight to personalised decisions.
We develop causal and reliable AI methods for personalised decision-making, with healthcare as a central application.
Our mission
Causal AI for better personalised decisions
We develop and apply causal AI methods to understand how interventions affect individuals, compare possible actions and support decisions under uncertainty.
Our work combines methodological research with real-world applications, particularly in healthcare, where reliable personalised decisions can improve treatment selection and clinical decision support.
Research
Research themes
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.
View our 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
Latest
Recent highlights
01
Area Chair at the NeurIPS 2026 AI4Science Workshop
Dr Vinod Kumar Chauhan will serve as an Area Chair for the AI4Science Workshop at NeurIPS 2026.
Visit NeurIPS 202602
Associate Editor, BioData Mining
Dr Vinod Kumar Chauhan is joining BioData Mining as an Associate Editor.
Visit BioData Mining03
Area Chair at the ICML 2026 AI4Science Workshop
Dr Vinod Kumar Chauhan served as an Area Chair for the AI4Science Workshop at ICML 2026.
Visit the ICML 2026 workshop04
Convener for a PhD viva examination
Dr Vinod Kumar Chauhan acted as a convener for a PhD viva examination in the department in June 2026.
05
Position paper on causal AI for personalised treatments
Our position paper, “Position: Causal AI for Personalised Treatments Needs Holistic Approach”, argues for a holistic approach to developing causal AI for patient-specific care.
Read the position paperSelected work
Featured publications
Position: Causal AI for Personalised Treatments Needs Holistic Approach
Sample Selection Bias in Machine Learning for Healthcare
Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes
Beyond Correlations: The Necessity and the Challenges of Causal AI
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Opportunities
Join us
We welcome researchers and students interested in advancing causal AI for personalised decision-making, including its application to healthcare.
PhD studentship opportunity
CAIH Lab is recruiting a PhD student in Causal AI for personalised healthcare. If you are interested, please get in touch at your earliest convenience to arrange a discussion.
Contact the lab