Welcome

I am an Associate Professor in Medical Biostatistics at the University of Limerick.

My research develops statistical, causal inference, and machine learning methods for healthcare decision-making using observational, routinely collected healthcare data and randomised controlled trials. I am particularly interested in estimating treatment effects, understanding treatment-effect heterogeneity, and developing methods that support personalised and population-level decision-making under uncertainty.

My work lies at the intersection of:

• Statistics

• Biostatistics

• Causal Inference

• Causal Machine Learning

• Mathematics

• Health Data Science

• Health Economics

• Electronic Health Records

• Healthcare Decision Science

Applications include polypharmacy and multipile long-term conditions, cardiovascular disease, diabetes, mental health, and other areas where evidence from randomised trials is incomplete or unavailable.

Current PhD Opportunity

Fully Funded PhD in Statistics, Causal Inference and Machine Learning

Applications are currently invited for a fully funded four-year PhD focused on developing novel statistical and causal machine-learning methods for dynamic treatment effects using longitudinal electronic health records.

The project will investigate:

• Target Trial Emulation

• Dynamic Treatment Regimes

• Semiparametric Inference

• Debiased Machine Learning

• Heterogeneous Treatment Effects

• Longitudinal Healthcare Data

Applications are encouraged from candidates with backgrounds in Statistics, Biostatistics, Mathematics, Data Science, Computer Science, Econometrics, Epidemiology, and related quantitative disciplines.

➡ See the PhD Opportunities page for details.

Research Vision

Healthcare systems increasingly rely on observational healthcare data to inform decisions that cannot be addressed through randomised trials alone.

My research aims to develop statistically rigorous methods capable of answering clinically relevant causal questions using real-world healthcare data while addressing challenges such as confounding, treatment heterogeneity, competing risks, dynamic treatment pathways, and limited treatment overlap.

The long-term goal is to improve evidence generation and support safer, more personalised, and more equitable healthcare decision-making.

Statistics is really about solving interesting and important problems and I think the development of new methodology and theory is important. But at the end of the day, it should be connected to real world applications.

Academic Background

Before joining the University of Limerick, I was a Research Associate in Statistics and Causal Inference at The University of Manchester (2022–2026), where I developed causal inference methods using large-scale linked electronic health records.

Previously, I worked as a postdoctoral researcher in biostatistics at the HRB Clinical Research Facility at the University of Galway (2019–2022), contributing to methodological research in causal inference and population health and developing open-source software including the R packages graphPAF and causalPAF.

I completed a PhD in Health Economics and Statistics at the University of Limerick in 2020 and hold a First Class Honours degree in Financial and Actuarial Mathematics from Dublin City University.