Delay-corrected RL operator with causal attribution

Standard constrained reinforcement learning assumes consequences are immediate and tied to the current action, but real-world violations are often delayed and stochastic. This can lead to penalizing the wrong action. The proposed CCPL framework introduces two key components: a delay-corrected Bellman operator that adapts the discount based on the consequence-delay distribution, and an Interventional Consequence Net that estimates each action's causal contribution rather than relying on temporal proximity. The contraction proof for the operator is a notable theoretical contribution, as it holds even when the delay distribution is unknown. However, the current reliance on a structural causal model for pretraining the ICN is a significant limitation. The approach has not yet demonstrated learning from purely observational or interventional data, which is crucial for practical deployment in many settings.