Derives sharp nested CDF envelopes for transported quantile treatment effects under marginal sensitivity bounds on confounding and transportability, with semiparametric estimators, uniform inference, and breakdown frontiers.
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Deep neural networks trained to classify simulated samples under null and alternative hypotheses produce a test statistic that outperforms nineteen competing methods for independence testing across varied dependence structures.
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Nested Sensitivity Envelopes for Transported Quantile Treatment Effects
Derives sharp nested CDF envelopes for transported quantile treatment effects under marginal sensitivity bounds on confounding and transportability, with semiparametric estimators, uniform inference, and breakdown frontiers.
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Deep-testing: the case of dependence detection
Deep neural networks trained to classify simulated samples under null and alternative hypotheses produce a test statistic that outperforms nineteen competing methods for independence testing across varied dependence structures.