Maxime
LEGER

PhD students



Director(s):

Subject: Biostatistics - Anesthesia - Intensive Care

Thesis subject

Research topic on causal inference from observational data (i.e., cohorts), focusing on the use of propensity scores based methods and their limitations, possible alternatives (G-Computation, TMLE), and the value of using data-adaptive methods (machine learning) to improve our estimations.
Illustration 'About-us'

Publications

icon publication

G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes.

Le Borgne F, Chatton A, Léger M, Lenain R, Foucher Y.

Sci Rep.
2021 Jan 14;11(1):1435. doi: https://doi.org/10.1038/s41598-021-81110-0.
icon publication

G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study.

Chatton A, Le Borgne F, Leyrat C, Gillaizeau F, Rousseau C, Barbin L, Laplaud D, Léger M, Giraudeau B, Foucher Y.

Sci Rep.
2020 Jun 8;10(1):9219. doi: 10.1038/s41598-020-65917-x.
Illustration publications

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