2 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.ML2025
Provable Recovery of Locally Important Signed Features and Interactions from Random Forest
Kata Vuk, Nicolas Alexander Ihlo, Merle Behr
Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their interactions in complex prediction mo…