Delay-corrected Bellman operator + causal attribution for constrained RL contraction proof under unknown stochastic delay [R]

Standard constrained RL assumes consequences are immediate and attributable to the current action. This breaks down whenever violations are delayed and stochastic, which is most real-world settings you end up penalizing whatever action happened to precede the observed violation, not the action that caused it.

Working on CCPL (Causal Consequence-Penalized Learning) to address this:

- A delay-corrected Bellman operator using an adaptive effective discount learned from the consequence-delay distribution. Contraction proof holds under unknown stochastic delay.

- An Interventional Consequence Net (ICN), pretrained on structural-causal-model labels, estimating marginal causal contribution per action for attribution rather than penalizing based on temporal proximity.

Limitations, to be upfront about them:
- The ICN currently requires access to the environment's structural causal model to generate pretraining labels it's not learned end-to-end from observational or interventional data alone. That's a real constraint on applicability outside benchmark settings where the SCM is known or can be reasonably specified.

Open to contributions and collaborators, especially if you work in constrained/safe RL or causal inference feel free to open an issue or reach out directly.