An adaptive smoothing method yields valid and asymptotically optimal inference for the mean outcome under an optimal treatment regime, achieving a derived lower bound on asymptotic variance for robust asymptotically linear unbiased estimators regardless of regularity.
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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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Optimal Inference of the Mean Outcome under Optimal Treatment Regime
An adaptive smoothing method yields valid and asymptotically optimal inference for the mean outcome under an optimal treatment regime, achieving a derived lower bound on asymptotic variance for robust asymptotically linear unbiased estimators regardless of regularity.
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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.