3 papers
stat.AP2026
Evaluating the influence of treatment-effect heterogeneity on discrimination
Florie Bouvier, Etienne Peyrot, Francois Petit +1
Analyzing the heterogeneity of treatment effects is crucial in personalized medicine to identify which patients will benefit from specific treatments. The performance of a conditio…
stat.ME2026
Choosing Covariate Balancing Methods for Causal Inference: Practical Insights from a Simulation Study
Etienne Peyrot, Raphaël Porcher, Francois Petit
Background: Inverse probability of treatment weighting (IPTW) is used for confounding adjustment in observational studies. Newer weighting methods include energy balancing (EB), ke…
stat.ME2025
Estimating Complier Average Causal Effects with Mixtures of Experts
François Grolleau, Céline Béji, Raphaël Porcher +1
Treatment non-compliance, where individuals deviate from their assigned experimental conditions, frequently complicates the estimation of causal effects. To address this, we introd…