提升因果预测的可靠选择，精准筛选高可信度个体
Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions
- 用伪结果构建双重稳健代理误差，克服异方差干扰
- 通过去噪对齐实现严格错误发现率控制，显著提升检测力
- 适合需高可靠性因果推断的医疗、金融等决策场景
在选择性部署中，从业者仅对模型选定的个体子集采取行动，基于预测的条件平均处理效应（CATE）。然而，边际协方差保证未必能控制该子集上的可靠性。本文研究黑箱CATE预测器的可靠选择：在保证错误发现率（FDR）控制的前提下，选出CATE误差低于容忍阈值的个体。由于CATE误差不可观测，我们从伪结果构造双重稳健的代理误差；但朴素代理误差在异方差下会因方差掩盖可靠性信号而失去效力。为此，我们提出去噪协方差对齐方法，通过减去估计的条件方差分量，并结合协方差校准与Benjamini--Hochberg选择策略。理论分析表明，有效性取决于代理/理想阈值标签的稳定性，而非方差估计的精确性。实验显示，在多种挑战性设置下，该方法显著提升了检测力，同时保持了严格的FDR控制。
原文摘要 · Abstract (English)
In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees need not control reliability on that selected subset. We study reliable selection for black-box CATE predictors: selecting candidates whose CATE errors are below a tolerance while controlling the false discovery rate (FDR). Since CATE errors are unobservable, we construct doubly robust proxy errors from pseudo-outcomes; however, naive proxies can lose power under heteroskedasticity because variance overwhelms the reliability signal. We propose Denoised Conformal Alignment, which subtracts an estimated conditional variance component and combines conformal calibration with Benjamini--Hochberg selection. Our analysis shows that validity is governed by stability of proxy/oracle threshold labels, rather than pointwise perfection of the variance estimator. Experiments show substantially improved power while maintaining FDR control across challenging settings.
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