A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
Biometrika , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PRx combines kernel weight localization with predictive recursion for fast semiparametric density regression, yielding consistent estimators for unmixed parameters and competitive performance at low computational cost.
citing papers explorer
-
In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
-
Fast Semiparametric Density Regression with Weight-localized Predictive Recursion
PRx combines kernel weight localization with predictive recursion for fast semiparametric density regression, yielding consistent estimators for unmixed parameters and competitive performance at low computational cost.