🤖 AI Summary
This work addresses the challenge of jointly learning causal structure and causal effects from observational data while providing unified support for both interventional and counterfactual queries within Pearl’s causal hierarchy. To this end, the authors propose TabPFN-CFM, the first causal foundation model based on the TabPFN architecture, which leverages synthetic data pretraining and multitask learning to simultaneously perform causal graph discovery, interventional effect estimation, and counterfactual reasoning. The model also effectively incorporates known prior structural information when available. Experimental results demonstrate that TabPFN-CFM significantly outperforms existing baselines in both causal structure learning and outcome prediction on real-world datasets, exhibiting strong generalization capabilities and superior overall performance.
📝 Abstract
We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from observational data, supports queries on all three levels of Pearl's Causal Hierarchy and uses known graph structure when available to improve predictions. TabPFN-CFM is trained on synthetic datasets, and generalises to real datasets, demonstrating improved performance over both structural and outcome prediction baselines.