cs.LG · 2606.26467 Copy arXiv ID · Jun 25, 2026 Save A Causal Foundation Model for Structure and Outcome Prediction Authors: Max Zhu , Martino Mansoldo , Ching-Hao Wang , Stefan Groha
Organizations: University of Cambridge, United Kingdom · GSK.ai.
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.
Explore similar work Sep 2, 2026 · Christopher Stith, Hossein Rahmani, Jesse C. Cresswell Causal Inferences Causal Foundation Models
Feb 16, 2026 · Arik Reuter, Anish Dhir, Cristiana Diaconu +6 Causal Foundation Models Causal Graph
May 29, 2026 · Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1 Causal Foundation Models Causal Reasoning
Sep 2, 2026 · cs.LG J/K move · Enter open · S save
Christopher Stith, Hossein Rahmani, Jesse C. Cresswell
Layer 6 AI, Toronto, Canada · TD Bank Group, Toronto, Canada
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a compatible estimator, and finally training it. Meanwhile, across diverse settings and modalities, much of machine learning has shifted to the paradigm of foundation models: networks pretrained once at scale and applied to new tasks without fine-tuning. Causal foundation models (CFMs) bring this paradigm to causal inference. CFMs are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates. This work provides a practical introduction to this emerging area. We summarize the necessary background in causal inference and machine learning before discussing CFMs. Throughout, we include example code and Jupyter notebooks.