3 papers
stat.ME2026
A Doubly Robust Machine Learning Approach for Disentangling Treatment Effect Heterogeneity with Functional Outcomes
Filippo Salmaso, Lorenzo Testa, Francesca Chiaromonte
Causal inference is paramount for understanding the effects of interventions, yet extracting personalized insights from increasingly complex data remains a significant challenge fo…
stat.ME2025
Sparse Bayesian Partially Identified Models for Sequence Count Data
Won Gu, Francesca Chiaromonte, Justin D. Silverman
In genomics, differential abundance and expression analyses are complicated by the compositional nature of sequence count data, which reflect only relative-not absolute-abundances…
stat.ME2025
Global p-Values in Multi-Design Studies
Guillaume Coqueret, Yuming Zhang, Christophe Pérignon +2
Replicability issues -- referring to the difficulty or failure of independent researchers to corroborate the results of published studies -- have hindered the meaningful progressio…