Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks

2026-09-04

AI Summary

为解决无线网络中因遗漏变量导致的隐藏混淆问题，提出CV-CCI方法结合观察数据与有限随机化数据以提供有效的反事实KPI预测。

Abstract

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.

Problem

• Confounding
• Counterfactual Inference
• Wireless Networks
• Telemetry

Innovation

• Confounding-Valid Counterfactual Conformal Inference
• General Synthetic-Powered Inference
• hidden confounding
• finite-sample coverage guarantees