A kernel-copula embedding statistic equals zero exactly when causal dependence between X and Y is stable and is strictly positive otherwise, with a near-linear estimator and convergence rates provided.
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A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
The weighted Holm procedure (WHP) based on ordered weighted p-values is uniformly more powerful than the weighted alternative Holm procedure (WAP) based on ordered raw p-values, with stronger optimality properties under FWER control.
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Detecting Changes in Causal Dependence with Kernels and Copulas
A kernel-copula embedding statistic equals zero exactly when causal dependence between X and Y is stable and is strictly positive otherwise, with a near-linear estimator and convergence rates provided.
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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.
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Weighted Holm Procedures: Theory, Properties, and Recommendations
The weighted Holm procedure (WHP) based on ordered weighted p-values is uniformly more powerful than the weighted alternative Holm procedure (WAP) based on ordered raw p-values, with stronger optimality properties under FWER control.