Causal Foundation Models

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Causal foundation models aim to reuse knowledge from pretraining to discover cause-and-effect structure or estimate causal effects on new datasets, often without fine-tuning. Common approaches train transformers or prior-data fitted networks on synthetic structural causal models, sometimes adding graph encoders, temporal data, or observational and interventional examples. They are being tested on tasks including tabular graph discovery, time-series analysis, and treatment-effect estimation, with evaluations spanning synthetic, semantic, and real-world data. A key open challenge is whether performance transfers across domains and observational regimes: results vary with benchmark design, causal assumptions, and overlap between pretraining and evaluation data.

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22 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation
Oct 23, 2026Wenxin Chen, Kyra Gan, Mingyu Lu +5Causal Effect Estimation Causal Foundation Models
Cornell University, New York City, NY · University of Washington, Seattle, WA · Columbia University, New York City, NY +2
PriMa-Causa: Causal Prescriptive Maintenance
Oct 22, 2026Felix SaretzkyCausal Inference Causal Effect Estimation
Unit-level aggregation breaks coverage in causal foundation models
Oct 16, 2026Gabe Schulman, Lucius E. J. Bynum, Yoav WaldCausal Foundation Models Causal Effect Estimation
New York University · Technion
Market Impact Estimation via Causal Foundation Models
Oct 8, 2026Dennis Thumm, Ying ChenQuantitative Finance Algorithmic Trading
National University of Singapore Singapore, Singapore
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
What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views
Sep 29, 2026Heejin Jung, Gyeongdeok Seo, Hoyoon Byun +2Causal Effect Estimation Causal Foundation Models
Yonsei University · University of Illinois Urbana–Champaign
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
Foundation Models for Partial Causal Identification
Aug 21, 2026Alexis Bellot, Anish DhirCausal Foundation Models Causal Effect Estimation
Independent · University College London
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
Interventional Time Series Priors for Causal Foundation Models
Mar 11, 2026Dennis Thumm, Ying ChenCausal Foundation Models Synthetic Data Generation
National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore
Large Causal Models for Temporal Causal Discovery
Feb 20, 2026Nikolaos Kougioulis, Nikolaos Gkorgkolis, MingXue Wang +4Time-Series Causal Discovery Causal Foundation Models
Institute of Applied & Computational Mathematics, FORTH · Computer Science Department, University of Crete · Huawei Ireland Research Centre, Dublin, Ireland
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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A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation

Wenxin Chen, Kyra Gan, Mingyu Lu, Zilin Jing+4

Cornell University, New York City, NY · University of Washington, Seattle, WA · Columbia University, New York City, NY+2

Causal Effect Estimation Causal Foundation Models

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Longitudinal causal effect estimation is complicated by time-varying confounding and treatment-confounder feedback loops. Existing methods generally require data-specific nuisance-model fitting, and many are tailored to prespecified treatment sequences or prediction horizons, leading to time-consuming computation at deployment. We introduce Policy In-Context Longitudinal causal Effect estimation with Prior-data Fitted Networks (PICLE-PFN), the first foundation model for longitudinal causal effect estimation that supports dynamic treatment policies. PICLE-PFN represents target policies through in-context history-decision demonstrations, providing a unified interface for a broad class of treatment policies. Pretrained over synthetic data sampled from longitudinal structural causal models, PICLE-PFN amortizes causal effect estimation across datasets, policies, and horizons. Given a new observational dataset and target policy, it directly predicts conditional average potential outcomes (CAPOs) in one forward pass without task-specific training or hyperparameter tuning. Across synthetic and semi-synthetic longitudinal benchmarks, PICLE-PFN achieves competitive RMSE and substantially improved uncertainty calibration with a fraction of deployment runtime, compared to state-of-the-art conventional estimators trained and tuned separately for each dataset.