learning causal graphs from dynamic interventional data (i.e., RL data)

We introduce a method that encourages LMs to leverage their pretrained knowledge during post-training.

We provide a counterfactual semantics for hybrid dynamical systems and prove that intervention preserves sufficient conditions for solution existence, uniqueness, and measurability.