Charles Rahal
Associate Professor
Charles is an associate professor in computational social science at the University of Oxford, where he works with colleagues at the Demographic Science Unit and Leverhulme Centre for Demographic Science (where he is part of the Senior Management Board).
Charles is a co-investigator at the ESRC funded Centre for Care (and on another ESRC Strategic Research Grant), and acts as the local network lead co-ordinator for the UK Reproducibility Network (as part of the steering of Reproducible Research Oxford).
He was previously a British Academy Postdoctoral Fellow. His training includes degrees, diplomas, and certificates in computational econometrics, economics, advanced research methods, and investment and finance.
Charles's research focuses on methodological innovations which uncover patterns in large-scale observational data with a focus on equality and equity. It is usually motivated by a desire to improve policies and public administration.
This most recently includes but is not limited to population-wide scientometric analysis, model evaluation in machine learning, and computational approaches to the life course (broadly defined). Charles has recently been involved in several successful funding applications (totalling around £12m) and has published in many of the world's leading journals.
He predominantly works in Python, Bash and TeX, and takes great pride in being able to generate policy impact - having won awards and commendations for contributions to the UK government Covid-19 policy response - all through open and reproducible research.
Recent publications
Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish.
Journal article
Zhang L. et al, (2026), Multivariate Behav Res, 1 - 20
On the unknowable limits to prediction.
Journal article
Yan J. and Rahal C., (2025), Nat Comput Sci, 5, 188 - 190
The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary outcomes
Journal article
RAHAL R. et al, (2025), PLoS ONE
The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary outcomes.
Journal article
Domingue BW. et al, (2025), PLoS One, 20
Capitalizing on a crisis: a computational analysis of all five million British firms during the Covid-19 pandemic.
Journal article
Muggleton N. et al, (2025), J Comput Soc Sci, 8
