A Causal Foundation Model for Structure and Outcome Prediction
Machine Learning
2026-06-25 v1
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.
Cite
@article{arxiv.2606.26467,
title = {A Causal Foundation Model for Structure and Outcome Prediction},
author = {Max Zhu and Martino Mansoldo and Ching-Hao Wang and Stefan Groha},
journal= {arXiv preprint arXiv:2606.26467},
year = {2026}
}
Comments
20 pages, 7 figures, 17 tables, 43rd ICML Workshop on Foundation Models for Structured Data