发表机构
EURECOM; Huawei Technologies Sweden AB（欧洲通信与网络研究院; 华为技术瑞典公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无线网络反事实KPIs的隐藏混淆问题，提出CV-CCI方法，结合观测与随机遥测数据，在保证覆盖性的同时提升预测集效率，优于现有基线。
AI 中文摘要
共形反事实推理使网络运营商能够利用记录的遥测数据可靠地回答关于网络运行的“假设”问题，这些答案通常以预测集的形式呈现，其包含在用户定义概率下替代控制动作下将观测到的关键绩效指标（KPIs）。一个关键挑战在于，记录的遥测数据可能会遗漏控制器使用的变量，导致隐藏混淆并使反事实分析的统计保证失效。原则上，此问题可通过随机遥测数据解决，该数据通过独立于网络状态分配控制动作来收集。然而，由于此类随机化可能会干扰正常运行，随机遥测数据通常较为稀缺，导致仅基于它的反事实分析会产生无信息的预测集。为应对这些挑战，我们提出了混淆有效反事实共形推理（CV-CCI），其通过通用合成驱动推理（GESPI）原理，将丰富的、可能存在混淆的观测遥测数据与有限的随机数据相结合。CV-CCI利用观测数据提高效率，同时借助随机数据在任意隐藏混淆下保留有限样本覆盖保证。在两个代表性无线接入网（RAN）控制任务上的实验表明，CV-CCI在隐藏混淆下仍保持有效性，且比最先进的混淆有效基线产生更高效的预测集。
英文摘要
Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take the form of prediction sets that contain, with a user-defined probability, the key performance indicators (KPIs) that would have been observed under alternative control actions. A key challenge is that logged telemetry may omit variables used by the controller, resulting in hidden confounding and invalidating the statistical guarantees of counterfactual analysis. In principle, this issue can be addressed using randomized telemetry, collected by assigning control actions independently of the network state. However, because such randomization may disrupt normal operation, randomized telemetry is typically scarce, causing counterfactual analysis based solely on it to produce uninformative prediction sets. To address these challenges, we propose Confounding-Valid Counterfactual Conformal Inference (CV-CCI), which combines abundant, potentially confounded observational telemetry with limited randomized data through the General Synthetic-Powered Inference (GESPI) principle. CV-CCI leverages observational data to improve efficiency while using randomized data to retain finite-sample coverage guarantees under arbitrary hidden confounding. Experiments on two representative radio access network (RAN) control tasks show that CV-CCI remains valid under hidden confounding while producing more efficient prediction sets than state-of-the-art confounding-valid baselines.
