Dr Maurice O’Connell is an Associate Professor in Medical Biostatistics in the School of Medicine at the University of Limerick.

Prior to joining the University of Limerick, Dr O’Connell was a Research Associate in Statistics and Causal Inference at The University of Manchester (2022–2026), where he developed novel statistical and causal inference methods using large-scale longitudinal electronic health records for primary care GP practices linked to e.g., hospitalisations and death records. He also contributed to teaching on the MSc in Health Data Science at The University of Manchester, delivering modules in causal inference, statistical modelling, and R programming, and has held a Visiting Researcher position at the University of Leeds. 

Dr O’Connell completed his PhD in Health Economics and Statistics at the School of Medicine, University of Limerick in January 2020. His doctoral research focused on the statistical and health economic evaluation of randomised controlled trials and cost-effectiveness microsimulation models, with an emphasis on estimating both average and individualised treatment effects. He subsequently worked as a postdoctoral researcher in biostatistics at the HRB Clinical Research Facility, School of Medicine, University of Galway (2019–2022), contributing to methodological developments in causal inference and population health, including R packages such as graphPAF and causalPAF. 

He has held lecturing and research roles across multiple institutions, including the University of Limerick and Munster Technological University, and holds a first-class honours BSc in Financial and Actuarial Mathematics from Dublin City University. Prior to his academic career, he trained and worked as an actuary and later completed a Postgraduate Diploma in Education from University College Cork. 

Maurice's research focuses on developing and applying advanced statistical, causal inference, health economic and causal machine learning methods to support personalised and population-level decision-making in healthcare. 

His work aims to estimate both individualised and policy-level treatment effects and to predict outcomes under different clinical interventions, including “what-if” scenarios such as different medication, lifestyle, or care pathways. His research has applications in many disease areas including multimorbidity and polypharmacy, cardiovascular disease, mental health, and diabetes. By integrating biostatistics, causal inference, health economics, and real-world data, he seeks to improve treatment strategies and inform clinical and policy decisions.

Medical Biostatistics, Causal inference, Causal machine learning, Statistical and Epidemiogical methodology, Statistical and health economic evaluation of randomised controlled trials, observational data and electronic health records, Mathematics. Statistical theory and applications. Maurice is interested in the development of statisical methodology and theory with applications in medicine and health and in applied collaboration with other scientists.