Counterfactual prediction skips the causal graph: a GNN reads observational samples directly, and a 10-node model transfers to 30 nodes
Counterfactual prediction can skip causal-graph estimation: CFGNP maps observational samples directly to a counterfactual distribution, achieves stronger predictive performance than existing approaches across synthetic and image-based benchmarks, and a model trained only on 10-node graphs transfers to graphs up to 30 nodes without retraining.