arXiv stat.ML
· Papers
Significance-First Splitting: Aligning Treatment Heterogeneity Detection with Honest Estimation
arXiv:2607.03999v2 Announce Type: replace-cross Abstract: Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty. Existing tree-based methods make an uneasy trade-off: significance-based approaches (Radcliffe and Surry 2011) identif