Skip to main content

Institute for Data Science and Artificial Intelligence


Causality is an area concerned with the problem of finding and quantifying the relationship between cause and effect.

If you're looking for a data scientist to work with on a research project or someone to discuss potential methodologies with for a research problem, then you search for the topic you need or alternatively use the A-Z button to search the full list of data scientists.


Member Research interests
Professor Jack Bowden  
Professor Siobhan Creanor  
Dr Sam Engle Causality, Bayesian Inference Statistical Modelling Frequentist Inference, Decision Theory
Professor Oliver Hauser

Causality, Game theory, Machine learning, Clinical trials, Time series analysis

Dr Christis Katsouris Causality, Network Analysis, Statistical Modelling, Time Series Analysis
Professor Mark Kelson

Causality, Statistical modelling, Machine Learning, Clinical trials, Frequentist inference

Dr Sebastian Kripfganz

Causality, Software Engineering, Time Series Analysis, Statistical Modelling, Frequentist Inference

Professor David Llewellyn

Causality, Machine Learning, Statistical Modelling, Clinical Trials, Precision medicine, Population-based and Clinical datasets

Professor Climent Quintana-Domeque  
Dr Nitzan Peri-Rotem Causality, Statistical modelling, Frequentist inference, Survival analysis
Dr David Richards Optimisation, Causality, Physical Modelling, Software Engineering, Machine Learning, Bayesian Inference, Time Series Analysis, Frequentist Inference, High Performance Computing, Statistical Modelling
Dr Dario Sansone Causality, Machine Learning
Dr Joerg Weber Causality, Machine Learning