The Paired Swap Permutation Test is an exact non-parametric procedure that compares explanatory power of two dependent predictors via symmetric within-subject swapping for categorical data and ECDF mapping for continuous data.
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Derives exact marginal likelihood under finite-support Huber contamination via Dirichlet-Beta priors and dynamic programming over count allocations.
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
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Exact Comparison of Explanatory Strength of Two Dependent Predictors
The Paired Swap Permutation Test is an exact non-parametric procedure that compares explanatory power of two dependent predictors via symmetric within-subject swapping for categorical data and ECDF mapping for continuous data.
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Marginal likelihoods for finite-support Huber contamination
Derives exact marginal likelihood under finite-support Huber contamination via Dirichlet-Beta priors and dynamic programming over count allocations.
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A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.