Constraining Shortcut Pathways: A Front-Door-Inspired Interaction Bottleneck for End-to-End Autonomous Driving

End-to-end autonomous driving exchanges information among traffic participants, map elements, and ego planning states through high-dimensional latent representations. Although expressive, these representations can also encode contextual cues that are predictive during training but weakly tied to physical interaction. When propagated across entities, such cues can form shortcut pathways that make planning sensitive to distribution-specific context. To constrain this information flow, we introduce the Causal Interaction Bottleneck (CIB), a front-door-inspired module for cross-entity interaction. For each source, CIB predicts type-specific kinematic or geometric hypotheses and associated mode probabilities. Each hypothesis is re-encoded before CIB performs target-conditioned attention, with the mode probabilities serving as priors. Raw source features do not bypass this mediated path. At the network level, this design enforces source-side structural mediation: newly transmitted source information can affect the target only through the mediator. Under deterministic mediation, the mediator cannot increase contextual information after conditioning on the physical anchor and entity type. We further establish stability of the resulting attention rule and derive explicit bounds for the discrete kinematic update. Across sequential, parallel, and iterative architectures, CIB consistently improves planning performance. Matched controls indicate that these gains are not fully explained by generic bottleneck regularization. Representation probes and paired interventions further show that CIB reduces sensitivity to appearance-only changes while preserving responses to behaviorally relevant physical changes.