output
20152025
most citedMachine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems

230 citations

Showing stat.MEShow all

6 papers · 1 filter

stat.ME2025

Calibrating confounding strength in sensitivity models for weighting estimators: a comparative review and a new method

Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich +2

Causal inference is only valid when its underlying assumptions are satisfied, one of the most central being the ignorability or unconfoundedness assumption. However, this hypothesi…

stat.ME2025★ 2 cited

Treatment Effect Estimation in Causal Survival Analysis: Practical Recommendations

Charlotte Voinot, Clément Berenfeld, Imke Mayer +2

The restricted mean survival time (RMST) difference offers an interpretable causal contrast to estimate the treatment effect for time-to-event outcomes, yet a wide range of availab…

stat.ME2024★ 1 cited

Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders

Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich +2

In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxin…

stat.ME2022

Variable selection in high-dimensional logistic regression models using a whitening approach

Wencan Zhu, Céline Lévy-Leduc, Nils Ternès

In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the cen…

stat.ME2020★ 1 cited

Bayesian dose-regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics

Emma Gerard, Sarah Zohar, Hoai-Thu Thai +3

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose (MTD) of a drug under a specific schedule. Evaluating drug-schedules aims at improving treatment saf…

stat.ME2020★ 1 cited

A variable selection approach for highly correlated predictors in high-dimensional genomic data

Wencan Zhu, Céline Lévy-Leduc, Nils Ternès

In genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. Regularized approaches are classically used to perform…