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.
Key publications
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Hidden heritability due to heterogeneity across seven populations.
Journal article
Tropf FC. et al, (2017), Nature human behaviour, 1, 757 - 765
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The rise of machine learning in the academic social sciences
Journal article
Rahal C. et al, (2024), AI & SOCIETY, 39, 799 - 801
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The Keys to Unlocking Public Payments Data
Journal article
RAHAL C., (2018), Kyklos
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Publisher Correction: Offshoring emissions through used vehicle exports
Journal article
Newman SJ. et al, (2024), Nature Climate Change, 14, 297 - 297
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Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries.
Journal article
Aburto JM. et al, (2022), International journal of epidemiology, 51, 63 - 74
Recent publications
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On the unknowable limits to prediction.
Journal article
Yan J. and Rahal C., (2025), Nature computational science, 5, 188 - 190
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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), Journal of computational social science, 8
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The Role of the Third Sector in Public Health Service Provision: Evidence from 25,338 heterogeneous procurement datasets
Journal article
RAHAL C. et al, (2024), Journal of the Royal Statistical Society: Series A (Statistics in Society)
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The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items.
Journal article
Domingue BW. et al, (2024), Psychometrika, 89, 1034 - 1054
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Voting Patterns, Mortality, and Health Inequalities in England: A replication and extension of Smith and Dorling (1996)
Preprint
Rahal C. et al, (2024)