4 papers
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…
crossfit: A Graph-Based Cross-Fitting Engine in R
Etienne Peyrot, François Petit
Cross-fitting is a key ingredient in many semiparametric estimation procedures, such as double/debiased machine learning (DML), enabling valid estimation of low-dimensional targets…
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…
Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data
Florie Bouvier, Etienne Peyrot, Alan Balendran +4
Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine lear…