Post-ADC inference supplies valid p-values and confidence intervals for data-dependent targets after active data collection by extending selective inference to correct for both adaptive sampling bias and post-hoc target selection, relying only on noise assumptions.
Post-selection adaptive inference for least angle regression and the lasso.arXiv preprint arXiv:1401.3889, 354
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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.
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Post-ADC Inference: Valid Inference After Active Data Collection
Post-ADC inference supplies valid p-values and confidence intervals for data-dependent targets after active data collection by extending selective inference to correct for both adaptive sampling bias and post-hoc target selection, relying only on noise assumptions.
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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.