Interventional Root‐Cause Analysis of Quality‐of‐Service Degradation in LEO Satellite‐User Assignment

Operators of low Earth orbit (LEO) access networks increasingly diagnose quality‐of‐service (QoS) degradation with observational machine‐learning attribution, yet when the candidate causes are produced by the resource‐management policy itself, observational attribution is unsafe: it assigns the degradation to factors that an operator cannot act on or that only proxy the true cause. We treat the satellite‐user assignment policy as the object of root‐cause analysis and use the assignment simulator as an intervention oracle, decomposing per‐user degradation into policy‐induced congestion and channel impairment through structural interventions, and contrasting interventional attribution with observational Shapley attribution and constraint‐based causal discovery. Across seven scenarios spanning three regions and assignment instances of up to half a million binary variables, policy‐induced congestion is the dominant degradation and the load‐balancing control recovers per‐user throughput by factors of 1.8 to 10 at fixed coverage, with paired tests that remain statistically significant under multiple‐comparison correction. Shapley attribution assigns the degradation to geography under its observational and interventional forms, and under the causal form in the main scenario. Constraint‐based discovery orients the load relation against its structural direction, and the rank correlation between link signal‐to‐noise ratio and delivered throughput inverts as offered load grows. The interventional analysis identifies the policy as the actionable cause and separates degradation that the policy can repair from degradation bounded by fleet capacity or by the channel, a distinction that observational root‐cause analysis does not provide.