CRAwDAD: Causal Reasoning Augmentation with Dual-Agent Debate

ArXi:2511.22854v2 Announce Type: replace When people reason about cause and effect, they often consider many competing "what if" scenarios before deciding which explanation fits best. Analogously, advanced language models capable of causal inference can consider multiple interventions and counterfactuals to judge the validity of causal claims. Crucially, this type of reasoning is less like a single calculation and like an internal dialogue between alternative hypotheses.