Develops Grenander-type and debiased machine learning estimators for the sublevel-set probability curve of the CATE function, shown to be non-pathwise differentiable, along with its piecewise linear approximation.
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stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A general nonparametric test for constancy of smooth function-valued parameters from conditional distributions is introduced, with a tractable limiting null distribution unlike many norm-based alternatives.
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Nonparametric inference for sublevel-set probabilities of conditional average treatment effect functions
Develops Grenander-type and debiased machine learning estimators for the sublevel-set probability curve of the CATE function, shown to be non-pathwise differentiable, along with its piecewise linear approximation.
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A general nonparametric framework for testing hypotheses about function-valued parameters
A general nonparametric test for constancy of smooth function-valued parameters from conditional distributions is introduced, with a tractable limiting null distribution unlike many norm-based alternatives.