CausalDrive: Counterfactual Causal Decomposition for End-to-End Autonomous Driving

End-to-end autonomous driving typically learns driving policies by imitating expert trajectories. However, observed expert trajectories inherently couple nominal driving intentions with multimodal responses to surrounding agents. Different combinations of these factors can produce similar trajectories, resulting in severe causal ambiguity that makes it difficult for data-driven methods to identify their respective contributions to the final trajectory. To address this ambiguity, we propose CausalDrive, a causal decomposition framework that separates driving policy learning into two sequential stages: deterministic nominal intention prediction and probabilistic interactive response modeling. Specifically, we first construct a counterfactual generator that produces nominal trajectories from navigation commands and map topology, providing independent supervision for our desighed Nominal Intention Planner. We then develop a diffusion-based Interactive Response Policy to model the multimodal residual between nominal and expert trajectories, thereby capturing the uncertainty of interaction-induced behavior. The outputs of the two stages are composed to produce the final interaction-aware trajectory. Extensive experiments demonstrate that CausalDrive improves overall trajectory quality and exhibits significant advantages in complex interactive driving scenarios.