Causal Graph Generation and Validation for Cognitive 6G Networks

In this paper, we investigate causal learning and discovery in 6G networking context towards the ambition of achieving cognitive and autonomous network. This paper introduces a novel two-phase causal graph generation and verification framework tailored for autonomous networks in the 6G era. While existing causal discovery techniques provide valuable insights into network KPIs and actions, they often lack the robustness and adaptability required for real-world deployments. To address these challenges, we propose a structured offline-online approach that enhances both the accuracy and practical utility of causal graphs. In the offline phase, we generate multiple candidate causal graphs by leveraging a combination of state-of-the-art causal discovery methods. This process allows us to systematically explore different causal structures while incorporating domain-specific constraints. In the online phase, we validate and refine these candidate graphs in a working network environment. By observing real network behavior, applying controlled interventions, and analyzing the impact on key network KPIs, we iteratively evaluate the reliability of the generated causal graphs. We implement and test our approach in a realistic network emulator, demonstrating its effectiveness, and the experimental results show that our method improves causal inference accuracy and intervention predictability.