5 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…
Clustering methods for Categorical Time Series and Sequences : A scoping review
Ottavio Khalifa, Viet-Thi Tran, Alan Balendran +1
Objective: To provide an overview of clustering methods for categorical time series (CTS), a data structure commonly found in epidemiology, sociology, biology, and marketing, and t…
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…
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects
Qiming Du, Gérard Biau, François Petit +1
We present new insights into causal inference in the context of Heterogeneous Treatment Effects by proposing natural variants of Random Forests to estimate the key conditional dist…