Leeds Beckett University - City Campus,
Woodhouse Lane,
LS1 3HE
A common analytical error for research that seeks causal explanation that stems from analysing constructed variables – such as BMI (constructed from weight and height) and change-scores (constructed from baseline and follow-up measures.
This talk will cover:
The Obesity Institute at Leeds Beckett University brings together academics from a range of disciplines across the university with policy makers, practitioners, and people living with or at risk of obesity and their families and carers, to coproduce innovative person-centred advances in obesity locally, nationally and internationally.
Ridda Ali, is a PhD student in Causal Data Science at the Leeds Institute for Data Analytics (LIDA). Ridda graduated with a First-Class Honours degree in Computer Science in 2019 before starting a 4-year ESRC funded integrated PhD and MSc in Data Analytics and Society.
As part of her PhD, she was accepted into the highly competitive Alan Turing Enrichment Scheme, which allowed her to enhance her current research by taking advantage of the facilities and opportunities available at The Alan Turing Institute and its partners. Ridda is multilingual and speaks English, Italian, Urdu, Hindi and Punjabi fluently.
Her research interests include observational data analysis, causal inference, epidemiology, and data science. Her research focuses on methodological and applied advances in the understanding of prediction and causal explanation, as well as their distinct differences in obesity research.
Professor Mark S Gilthorpe is a Professor of Statistical Epidemiology in the Carnegie School of Sport and Obesity Institute, Leeds Beckett University, and a Fellow of the Alan Turing Institute for Data Science and Artificial Intelligence, London.
Trained as a mathematical physicist, Mark's driving interest centres on improving our understanding of the observable world through modelling. Mark has fashioned a programme of interdisciplinary research that spans the gap between theoretical and applied data analytics.
Mark focuses on modelling complexity, highlighting and solving common analytical problems in observational research. His research and teaching interests have converged around the insights and utility of causal inference methods. His applied domain is around the causes and consequences of obesity within our society.
