6 papers
Targeted Active Learning for Preference-Based Treatment Effects on Multivariate Outcomes
Lola Giordani, Mathieu Even, Chloé Geoffroy +3
Treatment efficacy is traditionally demonstrated on the basis of a single primary outcome. However, clinical decision-making usually requires consideration of multiple outcomes, ba…
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…
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…
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…
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…