[论文] Waiting Buys Exactly Two Moments: Endogenous Identification in Predict...

论文概要

研究领域: ML 作者: Rama AlHamidi, Rasul Khanbayov, Erchin Serpedin, Hasan Kurban 发布时间: 2026-10-16 arXiv: 2610.23830

中文摘要

一个拥有固定预防性帮助预算的规划者必须决定帮助谁以及在何时帮助。等待可以揭示风险，但随着高风险个体率先离开，干预机会也在流失。我们在重复含噪信号和风险依赖流失的框架下刻画了这一权衡：在经典模型中，长度为 τ 的观察前缀恰好能识别潜在风险矩 m_j = E[p^j]（j=0,…,2τ-1），每增加一个观察期恰好买到两个新矩。这些矩可以识别存活者中下一期的平均流失率，但未必能识别分配福利或优先治疗人群的流失。我们的观测等价构造确立了在固定观察长度下任何样本量都不可避免的时序遗憾，而精确证书决定了所有兼容人群是否共享唯一最优日期。匹配实验分离了估计、Bootstrap 平均和遗憾加权的效果。更长的观察通常能解决时序模糊性，但最优日期可能已经过去。在我们的实验中，Bootstrap 平均和提前停止改进了决策，而遗憾加权在汇总均方遗憾上没有带来改进：更好的统计拟合未必产生更好的分配福利。

原文摘要

A planner with a fixed budget of preventive help must decide whom to help and when. Waiting reveals risk but removes intervention opportunities as higher-risk individuals leave first. We characterize this tradeoff under repeated noisy signals and risk-dependent attrition: in the canonical model, a length-\(τ\) observation prefix identifies exactly the latent risk moments \(m_j=\mathbb E[p^j]\) for \(j=0,\ldots,2τ-1\), buying two new moments per additional period. These identify average next-period attrition among survivors but need not identify allocation welfare or attrition among those prioritized for treatment. Our observationally equivalent constructions establish unavoidable timing regret at any sample size for a fixed observation length, while exact certificates determine whether all compa...

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