3 papers
stat.ML2026
Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias
Nicolas Alexander Ihlo, Merle Behr
In various fields, such as medicine and marketing, accurately predicting individual treatment effects holds significant promise. However, achieving reliable predictions alone is of…
stat.ME2025
Multi-state Models For Disease Histories Based On Longitudinal Data
Simon Wiegrebe, Johannes Piller, Mathias Gorski +4
Multi-stage disease histories derived from longitudinal data are becoming increasingly available as registry data and biobanks expand. Multi-state models are suitable to investigat…
stat.ML2025
Decorrelated feature importance from local sample weighting
Benedikt Fröhlich, Alison Durst, Merle Behr
Feature importance (FI) statistics provide a prominent and valuable method of insight into the decision process of machine learning (ML) models, but their effectiveness has well-kn…