CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions

Traffic prediction is a cornerstone of intelligent transportation, yet model reliability under sudden structural changes remains poorly understood. Existing evaluations quantify predictive degradation under structural interventions, but provide limited evidence on whether model representations register those changes or remain rigid when performance deteriorates. We introduce CausalGap, a diagnostic framework centered on the Representation Response Landscape, a two-dimensional state space defined by Dual-Metric Intervention Sensitivity (DMIS) and Relative Degradation (RD), which measure representation responsiveness and normalized post-intervention error increase, respectively. A constructive error-only impossibility example motivates joint analysis of response and degradation within a four-regime landscape, including pathological rigidity, characterized by high degradation and low responsiveness at a declared representation interface. To reduce structural-access confounding, CausalGap introduces a Dual-Track Structural Access Interface separating native and controlled access. The primary falsifiable test asks whether weak-intervention response provides incremental information about stronger unseen degradation beyond prespecified weak-error trajectory summaries and controls, complemented by an information-matched post-hoc steering test of explicit DMIS parameterization. The benchmark protocol supports topology-input stress testing and intervention-consistent response-failure analysis across heterogeneous traffic predictors and multiple observational, controlled, and simulation-based settings, while treating real-world disruption evidence separately when representation response is unavailable. Matched-error comparisons, invariance checks, and alternative response measures assess measurement stability and diagnostic consistency across model families and settings. CausalGap provides a diagnostic benchmark for representation-response analysis under structural interventions. Resources and evaluation instructions are available at https://anonymous.4open.science/r/CausalGap-80CB.