CausalTune: Causal Learning based Automated Cellular RAN Configuration Tuning Framework

Continual configuration tuning in cellular radio access networks (RANs) is critical for maintaining performance, reliability, energy efficiency, and user experience. However, this task remains largely manual in practice. Automating it needs to confront high-dimensional configuration spaces, sparse and biased exploration, strong parameter interactions, and substantial environmental confounding. Existing RAN configuration tuning approaches have limited effectiveness in addressing these challenges. In this paper, we present CausalTune, a novel causal learning framework for automated RAN configuration optimization based on observational telemetry. CausalTune disentangles configuration effects from environmental and operational confounders, generalizes to sparse and previously unseen parameter settings, and captures high-impact multi-parameter interactions. Our key insight is that effective causal inference in operational RANs requires reshaping raw telemetry to expose confounding and learning environment-invariant mechanisms. Guided by this insight, CausalTune employs a multistage pipeline that integrates distributional representation learning, causal modeling, and interaction-aware recommendation. We evaluate CausalTune using 10 months of RAN measurement data from 1M commercial cells of a major cellular operator. Our comparison to state-of-the-art baselines shows that CausalTune achieves up to 3X KPI improvement on the held-out dataset. In terms of causal modeling quality, CausalTune achieves up to 12X lower KPI reconstruction error; on recommended configuration safety, it achieves 4X higher agreement with expert engineers while significantly reducing off-target recommendations. These findings demonstrate the potential of causal learning to enable reliable, scalable, and interpretable RAN configuration tuning.