Training data attribution is increasingly used to filter, reweight, and prioritize rollouts in reinforcement learning with verifiable rewards (RLVR). However, these interventions often assume that individual samples, transitions, or trajectories can be attributed independently of the other data used in the same update. We show that this assumption is not merely a property of the attribution estimator, but a structural property of the training objective. We recast training data attribution as individual treatment effect estimation and use the Stable Unit Treatment Value Assumption (SUTVA) to determine when a proposed attribution unit is well-defined. This yields a simple criterion that attribution is only valid at the level where the algorithm assigns a stable contribution to that unit. Applying this criterion to RLVR, we show that the PPO objective admits trajectory-level attribution, whereas naive trajectory-only attribution is not well-defined for GRPO unless peer context is modeled. Experimental results support this distinction and provide preliminary evidence for group-level attribution under GRPO.