A Causal Foundation Model for Structure and Outcome Prediction

We introduce TabPFN-CFM, a causal foundation model that addresses causal structure learning and causal outcome estimation within a single amortized inference network. TabPFN-CFM is a prior-fitted network trained purely on synthetic structural causal models. Using only an observational dataset and, optionally, prior knowledge of the causal graph, it predicts the posterior causal structure, including unobserved confounding, and distributions for observational, interventional and counterfactual queries, covering all three levels of Pearl's Causal Hierarchy. Representing structure with acyclic directed mixed graphs lets the model express latent confounding, and we show that conditioning on a known graph can only improve predictive accuracy. Evaluated on in-distribution, out-of-distribution and semi-synthetic datasets with established causal graphs, TabPFN-CFM improves over meta-learner and existing prior-fitted-network baselines for interventional prediction and over both classical and deep structure learners for graph recovery, while also supporting counterfactual queries. A set of backbone and optimizer changes reduces the number of training steps needed to reach a target loss by roughly $4\times$.