mlsanityData to 7 Oct 2026

A Causal Foundation Model for Structure and Outcome Prediction

arXiv 2606.26467 ↗First posted 25 Jun 2026cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 25 Jun 2026.

Builds on

9 of 21 references.

References · 9

  1. 2020On Layer Normalization in the Transformer Architecturecited by 906
  2. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
  3. 2020Query-Key Normalization for Transformerscited by 266
  4. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  5. 2022Amortized Inference for Causal Structure Learningcited by 89
  6. 2022Learning to Induce Causal Structurecited by 68
  7. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 46
  8. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 43
  9. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 40

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Causal Foundation Modelscited by 0
  2. 2026CAFE: Counterfactual Prediction via Fast Posterior Estimationcited by 0