Introducing RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI

Wednesday, 27 May 2026, 11am to 12pm

For our next Data Engineering meeting, we will be hearing from Dr Charles Rahal, Associate Professor in Data Science and Informatics, LCDS, University of Oxford. We’re delighted to host Charles in what promises to be a great talk!

Date: Wednesday 27 May
Time: 11:00 – 12:00
Talk title: Introducing RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI
Location: Big Data Institute, Seminar Room 0
Registration: https://forms.office.com/e/mSfmZVXC9Z?origin=lprLink

Abstract
Scientific inference is often undermined by the vast but rarely explored `multiverse' of defensible modeling choices which can generate results as variable as the phenomena under study. We introduce RobustiPy, an open-source Python library that systematizes multiverse analysis and model-uncertainty quantification at scale. RobustiPy unifies bootstrap-based inference, combinatorial specification search, model selection and averaging, joint-inference routines, and explainable AI methods within a modular, reproducible framework. Beyond exhaustive specification curves, it supports rigorous out-of-sample validation and quantifies the marginal contribution of each covariate. We demonstrate its utility across five simulation designs and ten empirical and high-profile replications spanning economics, sociology, psychology, and medicine, including a re-analysis of widely cited findings with documented discrepancies. Benchmarking on ~672 million simulated regressions shows that RobustiPy delivers state-of-the-art computational efficiency while expanding transparency in empirical research. By standardizing and accelerating methods for robustness, RobustiPy transforms how researchers interrogate sensitivity across the analytical multiverse, offering a practical foundation for more reproducible and interpretable computational science.

Short biography
Charles is an Associate Professor in Data Science and Informatics at the University of Oxford, and a former British Academy Postdoctoral Fellow and a Sino-British Visiting Fellow at the University of Hong Kong. He's also an Honorary Visiting Professor at Peking University. He maintains various open and interactive online projects, such as the GWAS Diversity Monitor and RobustiPy, and is active in the Open Science movement. He has consulted on Methods Advisory Groups for the ONS (pro bono), and currently consults for the Banco de la República. He is both an Associate Member at Nuffield College and a Researcher at the Gradel Institute, New College, as well as an Associate Editor-In-Chief at the Journal of Social Computing, and an Associate Editor at ACM Transactions on Social Computing. He is a moderator of SocArXiv, as well as a Research Affiliate at the Public Knowledge Project, while also convening the ‘Metrics and Models’ lab, which has an open seminar series component to it; all are welcome to attend.

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All members of the University are welcome to join, please let reception at BDI know you’re here for the Data Engineers meeting and sign-in. We hope you can join us!

Microsoft Teams meeting
Link to be shared nearer the time
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Speaker(s): Dr Charles Rahal (LCDS, University of Oxford)

Venue: Big Data Institute - Lower Ground Seminar Room 0 - Lower Ground Seminar Room 0 Big Data Institute Old Road Campus Oxford Oxfordshire OX3 7LF United Kingdom

Department: Big Data Institute - NDPH (Unit)

Organiser: Sumeeta Maheshwari

Host: Prof Thomas Nichols

Register here: https://forms.office.com/e/mSfmZVXC9Z?origin=lprLink