让回归预测区间同时具备可解释性和可靠覆盖率。
CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression
- 先构建可解释的置信包络，再用分片校准保证覆盖性。
- 区间宽度分解为随机误差、认知不确定性与校准余量。
- 适合需要可信预测区间的工业落地场景。
共形预测能提供无分布假设的覆盖保证，但在模型外推区域可能表现过度自信，因标准共形得分未显式体现认知不确定性。而可信方法虽能显现认知影响，却通常依赖模型且缺乏校准保障。我们提出CREDO，一种简单的“先建可信包络，再共形化”方法：首先构建随局部证据减弱而扩大的可解释可信包络；随后在该包络上应用分片共形校准，无需额外假设即可保证边际覆盖。该分离设计使预测区间具有可解释性——其宽度可分解为随机噪声、认知膨胀和分布无关的校准松弛。我们提供了基于截断后验预测端点的快速实现，证明了方法有效性，并在基准回归任务中显示，CREDO在保持目标覆盖率的同时，提升了稀疏适应性，且效率具竞争力。
原文摘要 · Abstract (English)
Conformal prediction delivers prediction intervals with distribution-free coverage, but its intervals can look overconfident in regions where the model is extrapolating, because standard conformal scores do not explicitly represent epistemic uncertainty. Credal methods, by contrast, make epistemic effects visible by working with sets of plausible predictive distributions, but they are typically model-based and lack calibration guarantees. We introduce CREDO, a simple "credal-then-conformalize" recipe that combines both strengths. CREDO first builds an interpretable credal envelope that widens when local evidence is weak, then applies split conformal calibration on top of this envelope to guarantee marginal coverage without further assumptions. This separation of roles yields prediction intervals that are interpretable: their width can be decomposed into aleatoric noise, epistemic inflation, and a distribution-free calibration slack. We provide a fast implementation based on trimming extreme posterior predictive endpoints, prove validity, and show on benchmark regressions that CREDO maintains target coverage while improving sparsity adaptivity at competitive efficiency.
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