Causal Foundation Models

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
Sep 30, 2026Yuche Gao, Arik Reuter, Siyuan Guo +3Causal Foundation Models Causal Inference
University of Cambridge, Cambridge, United Kingdom · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Prior Labs +3
Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining
Sep 22, 2026Zhiheng ZhangCausal Foundation Models Synthetic Data Pretraining
School of Statistics and Data Science Shanghai University of Finance and Economics
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Sep 10, 2026Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1Causal Foundation Models Structural Causal Models
School of Artificial Intelligence, Nanjing University
Causal Foundation Models
Sep 2, 2026Christopher Stith, Hossein Rahmani, Jesse C. CresswellCausal Foundation Models Causal Effect Estimation
Layer 6 AI, Toronto, Canada · TD Bank Group, Toronto, Canada
DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms
Jul 13, 2026Yikang Chen, Zhengkang Guan, Haoyuan Qian +3Causal Foundation Models Tabular Foundation Models
1Zhejiang University · 2Tsinghua University
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
Jul 13, 2026Jie Qiao, Ruichu Cai, Zijian Li +6Causal Foundation Models Causal Discovery
School of Computer Science, Guangdong University of Technology, Guangzhou, China · College of Mathematics and Computer, Shantou University, Shantou, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China
A Causal Foundation Model for Structure and Outcome Prediction
Jun 25, 2026Max Zhu, Martino Mansoldo, Ching-Hao Wang +1Causal Foundation Models Causal Structure Learning
University of Cambridge, United Kingdom · GSK.ai.
DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
Jun 19, 2026Zhengkang Guan, Yikang Chen, Yi He +5Causal Foundation Models Structural Causal Models
College of Computer Science and Technology Zhejiang University
Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signals
Jun 18, 2026Shravan Talupula, Saurabh SharmaCausal Foundation Models Causal Effect Estimation
ProfitOps Inc.
TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery
May 29, 2026Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1Causal Foundation Models Synthetic Data Pretraining
Nanjing University
Towards Continuous-time Causal Foundation Models
May 26, 2026Dennis Thumm, Ruben Wiedemann, Ying ChenIrregular Time-Series Modeling Causal Foundation Models
National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore. · Imperial College London
Causal Foundation Models with Continuous Treatments
May 14, 2026Christopher Stith, Medha Barath, Vahid Balazadeh +2Causal Foundation Models Causal Effect Estimation
Layer 6 AI · University of Toronto · Vector Institute
Arrow: A Foundation Model for Causal Discovery
May 8, 2026Ryan Thompson, He Zhao, Daniel M. Steinberg +1Causal Foundation Models Tabular Foundation Models
University of Technology Sydney · CSIRO’s Data61
Use What You Know: Causal Foundation Models with Partial Graphs
Feb 16, 2026Arik Reuter, Anish Dhir, Cristiana Diaconu +6Causal Foundation Models Causal Discovery
University of Cambridge, Cambridge, United Kingdom · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Gatsby Computational Neuroscience Unit, University College London +5

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CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes

Yuche Gao, Arik Reuter, Siyuan Guo, Anish Dhir+2

University of Cambridge, Cambridge, United Kingdom · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Prior Labs+3

Causal Foundation Models Causal Inference Causal Intervention

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Causal foundation models (CFMs) amortise causal inference over priors of synthetic structural causal models (SCMs), predicting the effect of an experiment on a specific variable. However, observational data alone may leave multiple causal models compatible with available evidence, while experimental data with interventions on exactly the variable of interest might be unavailable. This work studies CFMs as a method to combine finite observational and surrogate-interventional datasets in order to predict a target conditional interventional distribution (CID) more accurately than with observational data alone. We first formalise the conceptual benefits of surrogate experiments. Building on this analysis, we introduce \textsc{Foundation Models for Causal Inference from Diverse Experimental Regimes} (\emph{CIDER-FM}), a causal foundation model that uses an intervention-aware representation and hierarchical three-axis attention to exchange information across variables, samples, and experimental regimes. We evaluate CIDER-FM against a wide range of baselines across diverse synthetic graph and mechanism families, as well as on both simulated and real-world data from Causal Chambers. Our results demonstrate strong CID prediction performance and show that incorporating experimental context can improve predictions over observational data alone.

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