用神经网络改进个体治疗效果的边界估计，更准更稳。
Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
- 设计锚定神经架构，确保概率约束自动满足
- 引入精度修正方法，消除极值偏差导致的区间过窄
- 适合高维数据下需要可靠因果推断的研究者
个体治疗效应无法从数据中精确识别。概率必要性与充分性（PNS）通过结合实验与观察数据得出的交集边界来刻画个体层面的因果关系，规避了这一限制。然而在有限样本下，传统插补估计器会系统性失效：违反结构概率约束，并因极大极小算子产生极值偏差，导致区间过窄。本文提出一种神经框架，用于有限样本下的PNS估计，同时解决这两类问题。引入锚定神经架构，保证结构约束在构造上被满足；为纠正极值偏差，采用精度修正的交集边界推断，利用认知神经网络实现可扩展的高维不确定性量化。实证评估表明，该方法在高维情形下仍能保持名义覆盖率与精确约束有效性，而传统估计器则系统性地低估。
原文摘要 · Abstract (English)
Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by characterizing individual-level causality through intersection bounds derived from combined experimental and observational data. In finite samples, however, standard plug-in estimators systematically fail: they violate structural probability constraints and suffer from extremum bias induced by max-min operators, yielding spuriously narrow intervals. We propose a neural framework for finite-sample PNS estimation that resolves both pathologies. We introduce an anchored neural architecture that guarantees structural constraint satisfaction by construction. To correct extremum bias, we employ precision-corrected intersection-bound inference, leveraging Epistemic Neural Networks for scalable, high-dimensional uncertainty quantification. Empirical evaluations confirm that this approach maintains nominal coverage and exact constraint validity in high-dimensional regimes where standard estimators systematically undercover.
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