The Open Radio Access Network (O-RAN) architecture enables a flexible, vendor-neutral deployment of 5G networks by disaggregating base station components and supporting third-party xApps for near real-time RAN control. However, the concurrent operation of multiple xApps can lead to conflicting control actions, which may cause network performance degradation. In this work, we propose a framework for xApp conflict management that combines explainable machine learning and causal inference to evaluate the causal relationships between RAN Control Parameters (RCPs) and Key Performance Indicators (KPIs). We use model explainability tools such as SHAP to identify RCPs that jointly affect the same KPI, signaling potential conflicts, and represent these interactions as a causal Directed Acyclic Graph (DAG). We then estimate the causal impact of each of these RCPs on their associated KPIs using metrics such as Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE). This approach offers network operators guided insights into identifying conflicts and quantifying their impacts, enabling more informed and effective conflict resolution strategies across diverse xApp deployments.

翻译：开放无线接入网络（O-RAN）架构通过解耦基站组件并支持第三方xApp进行近实时RAN控制，实现了灵活、供应商中立的5G网络部署。然而，多个xApp的并发运行可能导致控制动作冲突，进而引发网络性能下降。在本工作中，我们提出一个结合可解释机器学习与因果推断的xApp冲突管理框架，用于评估RAN控制参数（RCP）与关键性能指标（KPI）之间的因果关系。我们利用SHAP等模型可解释性工具识别共同影响同一KPI的RCP，以此指示潜在冲突，并将这些交互关系表示为因果有向无环图（DAG）。随后，我们使用平均处理效应（ATE）和条件平均处理效应（CATE）等度量指标，估计每个RCP对其关联KPI的因果影响。该方法为网络运营商提供了识别冲突并量化其影响的指导性见解，从而能够在多样化的xApp部署中制定更具信息依据且更有效的冲突解决策略。