I am an assistant professor (CCA-AHU) in the METHODS team at the Center for Research in Epidemiology and Statistics (CRESS-UMR1153), where my work focuses on machine learning and applied clinical research, with an emphasis on causal inference in the counterfactual framework (Rubin causal model). My research focuses in particular on the study of individual and average treatment effects, the use of observational data and compliance issues.
I am actively involved in the international MORE-Europa projects (More Effectively Using Registries to Support Patient-centered Regulatory and HTA Decision-making), funded by Horizon Europe. I am also involved in Deeptech innovation and entrepreneurship, having received the i-PhD 2023 award for the MyTreatment project. This initiative develops deep learning algorithms to support personalised cancer treatment prescriptions, enabling doctors to tailor therapies to individual patient needs.
Research interests
- Causal Inference, Counterfactual, Treatment Effect
- Methods for Personalised Medicine
- Deeptech Entrepreneurship and Innovation
Key publications
- Beji, C. (2021). Causal Populations Identification througth Hidden Distributions Estimation. Thèse de doctorat.
View publication - Beji, C. & Yger, F.n & Atif, J. (2021). Non parametric estimation of causal populations in a counterfactual scenario. Causal-AI 2021
View publication - Beji, C., Bon, M., Yger, F., & Atif, J. (2020). Estimating individual treatment effects through causal populations identification. ESANN 2020.
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