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Life sciences · Preprint
arXiv · September 4, 2026
Raises a question worth testing. It does not answer one.
This is a preprint describing CV-CCI, a novel statistical algorithm combining observational and randomized telemetry to enable counterfactual inference under hidden confounding in wireless network control. The work is computational and methodological, validated only on simulated representative tasks, and has not undergone peer review. It raises questions about validity and efficiency of confounding-robust prediction in network optimization but does not yet constitute evidence suitable for clinical or operational deployment.
Methodological algorithm development with simulated experiments. Radio access network (RAN) control systems; not human or clinical population. Intervention: Confounding-Valid Counterfactual Conformal Inference (CV-CCI) algorithm leveraging observational and randomized telemetry. Compared with: State-of-the-art confounding-valid baselines.
CV-CCI combines abundant potentially confounded observational telemetry with limited randomized data via GESPI principle Algorithm remains valid under hidden confounding while producing more efficient prediction sets than state-of-the-art baselines on two representative RAN control tasks Experiments demonstrate improved efficiency of prediction sets compared to confounding-valid baselines
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a methodological preprint proposing a novel statistical algorithm for counterfactual inference in wireless networks, with no peer review, clinical validation, or real-world deployment evidence.
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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.
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