A Causal Foundation Model for Structure and Outcome Prediction
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Computer Science > Machine Learning
Title:A Causal Foundation Model for Structure and Outcome Prediction
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.
| Comments: | 20 pages, 7 figures, 17 tables, 43rd ICML Workshop on Foundation Models for Structured Data |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.26467 [cs.LG] |
| (or arXiv:2606.26467v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26467
arXiv-issued DOI via DataCite (pending registration)
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