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stat.ME2026
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
A regularized multi-state model for covariate selection with interval-censored survival data
Ariane Bercu, Agathe Guilloux, Cécile Proust-Lima +1
In population-based cohorts, disease diagnoses are typically censored by intervals as made during scheduled follow-up visits. The exact disease onset time is thus unknown, and in t…
stat.ME2025
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…