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Model-free selective inference under covariate shift via weighted conformal p-values
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abstract
This paper introduces novel weighted conformal p-values and methods for model-free selective inference. The problem is as follows: given test units with covariates $X$ and missing responses $Y$, how do we select units for which the responses $Y$ are larger than user-specified values while controlling the proportion of false positives? Can we achieve this without any modeling assumptions on the data and without any restriction on the model for predicting the responses? Last, methods should be applicable when there is a covariate shift between training and test data, which commonly occurs in practice. We answer these questions by first leveraging any prediction model to produce a class of well-calibrated weighted conformal p-values, which control the type-I error in detecting a large response. These p-values cannot be passed on to classical multiple testing procedures since they may not obey a well-known positive dependence property. Hence, we introduce weighted conformalized selection (WCS), a new procedure which controls false discovery rate (FDR) in finite samples. Besides prediction-assisted candidate selection, WCS (1) allows to infer multiple individual treatment effects, and (2) extends to outlier detection with inlier distributions shifts. We demonstrate performance via simulations and applications to causal inference, drug discovery, and outlier detection datasets.
Forward citations
Cited by 7 Pith papers
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ACS: An interactive framework for conformal selection
ACS provides finite-sample false discovery rate control for interactive, adaptive selection of promising candidates from unlabeled pools.
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Certify or Refuse: A Cross-Model Map for Selective Risk Control with Coverage Floors under Covariate Shift
Under bounded-ratio covariate shift, a certified coverage floor creates a two-resource sample-complexity map, with a provable impossibility over the full unknown-weight class.
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WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales
WCTMs generalize conformal test martingales to test non-exchangeability nulls, enabling adaptation to mild covariate shifts, fast detection of harmful shifts, and root-cause diagnosis.
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Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization
OptCS allows conformity scores to be optimized on calibration and test data while preserving finite-sample false discovery rate control in conformal selection.
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Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection
GAIF dynamically adjusts testing thresholds with feedback for finite-sample FDR control in sequential settings and extends to conformal selection via feedback-driven model selection.
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Online Conformal Selection with Accept-to-Reject Changes
OCS-ARC is the first conformal selection method for online Accept-to-Reject Changes settings, controlling FDR at every timestep by feeding conformal p-values into online Benjamini-Hochberg.
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SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes
SAFER combines tabular EHR and clinical notes to make treatment recommendations with a claimed conformal FDR guarantee, but the proof and evaluation do not support the formal assurances.
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