Distribution-free predictive inference for individual treatment effects is impossible: any valid set must have infinite expected length under standard assumptions with continuous covariates.
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Theoreticalfoundations of conformal prediction
17 Pith papers cite this work. Polarity classification is still indexing.
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OCULAR calibrates dynamics uncertainty using perception from similar environments to give guaranteed prediction regions for unseen test conditions.
Two novel online conformal prediction algorithms enforce nested prediction sets across coverage levels using online optimization with regret bounds for quantile error control.
Trade-off functions between two distributions are finitely testable if and only if their Neyman-Pearson rejection regions are attainable by a VC-class of sets.
Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
Split conformal clustering with stochastic labels provides finite-sample marginal coverage guarantees for cluster label confidence sets, controlled by soft-label consistency and replace-one stability of the clustering algorithm.
Conformal risk control for bounded non-monotone losses over a grid of size m achieves excess risk of order sqrt(log m / n) with n calibration samples, which is minimax optimal.
A unified framework derives non-asymptotic bounds on conditional miscoverage in conformal prediction via pointwise and L_p routes and gives a common view of existing methods.
A quantized model exchange framework for decentralized conformal novelty detection preserves conditional exchangeability and delivers finite-sample global FDR control.
A PIT-calibrated percentile interval method delivers finite-sample marginal coverage, asymptotic conditional coverage, and shorter intervals than prior conformal approaches.
An approximate inequality for the probability involving order statistics under near-i.i.d. conditions is established and applied to justify resampling-based statistical procedures.
Conformal inference produces robust prediction intervals for treatment effects under experimental attrition, outperforming complete-case, imputation, and weighting approaches in simulations.
Venn-Abers predictors are extended to unbounded regression via conformal prediction, producing point regressors that modestly improve efficiency over standard methods for large datasets.
Conformalized super learner builds prediction intervals by weighting conformity scores from base learners via a majority vote, delivering valid coverage for continuous outcomes under exchangeability and heterogeneity.
Formal connections between PAC bounds for three data-driven reachability methods are established, with empirical results showing they are not interchangeable despite similarities.
Conformalized quantile regression applied post hoc to neutron star posterior samples yields reliable uncertainty bands validated by empirical coverage studies.
The paper experimentally studies cross-conformal e-prediction and conceptually simpler modifications for aggregating conformal e-predictors while retaining validity.
citing papers explorer
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Online Conformal Prediction: Enforcing monotonicity via Online Optimization
Two novel online conformal prediction algorithms enforce nested prediction sets across coverage levels using online optimization with regret bounds for quantile error control.
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Risk-Controlled Post-Processing of Decision Policies
Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
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Conformal Risk Control under Non-Monotone Losses: Theory and Finite-Sample Guarantees
Conformal risk control for bounded non-monotone losses over a grid of size m achieves excess risk of order sqrt(log m / n) with n calibration samples, which is minimax optimal.
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Decentralized Conformal Novelty Detection via Quantized Model Exchange
A quantized model exchange framework for decentralized conformal novelty detection preserves conditional exchangeability and delivers finite-sample global FDR control.
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Conformalized Percentile Interval: Finite Sample Validity and Improved Conditional Performance
A PIT-calibrated percentile interval method delivers finite-sample marginal coverage, asymptotic conditional coverage, and shorter intervals than prior conformal approaches.
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Conformalized Super Learner
Conformalized super learner builds prediction intervals by weighting conformity scores from base learners via a majority vote, delivering valid coverage for continuous outcomes under exchangeability and heterogeneity.