Researcher

Title

• Assistant Professor, Department of social and preventive medicine, Université de Montréal (2025-)
• Researcher, Centre de recherche Azrieli du CHU Sainte-Justine (2025-)

• Postdoctoral Fellowship, Université Laval (2025)
• Postdoctoral Fellowship, Université de Montréal (2024)
• PhD in Biostatistics, Université de Nantes, France (2021)
• MSc in Biostatistics, Université de Nantes, France (2018)
• BSc in physiology, Université de Nantes, France (2016) 

Arthur Chatton’s works are as the crossroad of prediction and causation, with an emphasis on the use of super learning approaches. Throughout his research journey, Arthur Chatton has developed a personalized dynamic prediction algorithm in dialysis and a framework to check the positivity assumption in various settings (e.g., longitudinal data, mediation analysis). More broadly, he’s interested in survival analysis, causal mediation, transportability and knowledge dissemination between disciplines.

The FACTS lab aims to (i) evaluate and improve the quality, usability and generalizability of predictive score for personalizing medical care and (ii) develop and evaluate new causal methods that consider the imperfection of data collected and used for research purposes.

Laboratory

FACTS - FActuals and Counterfactuals predictionS in health

• Biostatistics
• Causal inference
• Predictive modelling
• Dynamic prediction
• Machine learnng
• Super learning
• Survival analysis
• Causal mediation analysis
• Simulation studies
• Longitudinal data

Career Summary

Arthur Chatton is an assistant professor in Biostatistics at the Department of Preventive and Social Medicine of the École de santé publique de l'Université de Montréal, and a researcher at the Centre de recherche Azrieli du CHU Sainte-Justine. He holds a PhD in Biostatistics from Nantes University (France) and completed two postdoctoral fellowships at Université de Montréal and Université Laval.

Awards and Distinctions

• Postdoctoral Scholarship CRM-StatLab-CANSSI (2024)
• Daniel Schwartz Award for best doctoral thesis in statistics defended in 2021 and 2022. Société Française de Biométrie (2023)
• IVADO Postdoctoral Scholarship (2022)

Presentations

1. A diagnostic tool for positivity violations in mediation analyses (2025) WNAR/IMS Conference, Whistler, BC, Canada. Présentation invitée
2. Statistical tools for estimation: propensity scores, g-computation, doubly robust methods, marginal structural models (2025) Inserm workshop 282 - Best practices and recent advances in causal analyses, French National Institute for Health and Medical Research, Bordeaux, France. Présentation invitee
3. Sequential positivity checking for longitudinal causal inference (2024) Statistical Society Canada Annual Meeting, St. John’s, NF, Canada. Présentation invitée
4. Personalised dynamic super learning: an application in predicting hemodiafiltration’s convection volumes (2024) Sixth International Chinese Statistical Association - Canada Chapter Symposium, Niagara Falls, ON, Canada. Présentation invitée
5. Identifying individuals causing positivity violations as missing exclusion criteria: a decision trees-based algorithm (2023) Society for Epidemiologic Research Annual Conference, Portland, OR, USA.

Publications

1. Chatton A, Schomaker M, Luque-Fernandez M-A, Platt RW, and Schnitzer ME. (2025) Is the sequential positivity assumption getting you down? Try sPort! Accepté dans Epidemiology
2. Chatton A, Bally M, Lévesque R, Malenica, I, Platt RW, and Schnitzer ME. (2024) Personalised dynamic super learning: an application in predicting convection volumes in hemodiafiltration. Journal of the Royal Statistical Society, Series C
3. Chatton A and Rohrer JM. (2024) The causal cookbook: recipes for propensity score, g-computation and doubly robust standardisation. Advances in Methods and Practices in Psychological Science, 7(1)
4. Danelian G, Le Borgne F, Léger M, Foucher Y, and Chatton A. (2023) Identification of in-sample positivity violations using regression trees: the PoRT algorithm. Journal of Causal Inference, 11(1):20220032
5. Chatton A, Le Borgne F, Leyrat C, and Foucher Y. (2022) G-computation and doubly robust standardisation for continuous-time data: a comparison with inverse probability weighting. Statistical Methods in Medical Research, 31(4):706-718
6. Léger M, Chatton A, Le Borgne F, Pirracchio R, Lasocki S, and Foucher Y. (2022) Causal inference in case of near-violation of positivity: comparison of methods. Biometrical Journal, 64(8):1389-1403
7. Chatton A, Le Borgne F, Leyrat C, Gillaizeau F, Rousseau C, Barbin L, et al. (2020) G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study. Scientific Reports, 10(1):9219.
8. Féray C, Taupin J-L, Sebagh M, ..., Chatton A, et al. (2021) Donor HLA Class 1 Evolutionary Divergence Is a Major Predictor of Liver Allograft Rejection. Annals of Internal Medicine, 174(10):1385-1394
9. Lejeune F, Chatton A, Laplaud D-A, Le Page E, Wiertlewski S, Edan G, et al. (2021) SMILE: a predictive model for Scoring the severity of relapses in MultIple scLErosis. Journal of Neurology, 268(2):669–679
10. Brodeur, A., Valenta, D., Marcoci, A., …, Chatton A, et al. (2025). Comparing Human-Only, AI-Assisted, and AI-Led Teams on Assessing Research Reproducibility in Quantitative Social Science (I4R Discussion Paper Series 195). The Institute for Replication (I4R).