Saved in:
| Main Authors: | Reuter, Arik, Dhir, Anish, Diaconu, Cristiana, Robertson, Jake, Ossen, Ole, Hutter, Frank, Weller, Adrian, van der Wilk, Mark, Schölkopf, Bernhard |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.14972 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Do-PFN: In-Context Learning for Causal Effect Estimation
by: Robertson, Jake, et al.
Published: (2025)
by: Robertson, Jake, et al.
Published: (2025)
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
by: Dhir, Anish, et al.
Published: (2025)
by: Dhir, Anish, et al.
Published: (2025)
Bivariate Causal Discovery using Bayesian Model Selection
by: Dhir, Anish, et al.
Published: (2023)
by: Dhir, Anish, et al.
Published: (2023)
A Meta-Learning Approach to Bayesian Causal Discovery
by: Dhir, Anish, et al.
Published: (2024)
by: Dhir, Anish, et al.
Published: (2024)
The relative value of interventional and observational samples in Bayesian Causal Linear Gaussian Models
by: Lungu, Valentinian, et al.
Published: (2026)
by: Lungu, Valentinian, et al.
Published: (2026)
Semistable reduction of covers of degree $p$
by: Ossen, Ole
Published: (2024)
by: Ossen, Ole
Published: (2024)
Semistable reduction of plane quartics at $p=3$
by: Ossen, Ole
Published: (2025)
by: Ossen, Ole
Published: (2025)
Continuous Bayesian Model Selection for Multivariate Causal Discovery
by: Dhir, Anish, et al.
Published: (2024)
by: Dhir, Anish, et al.
Published: (2024)
FairPFN: A Tabular Foundation Model for Causal Fairness
by: Robertson, Jake, et al.
Published: (2025)
by: Robertson, Jake, et al.
Published: (2025)
In-Context In-Context Learning with Transformer Neural Processes
by: Ashman, Matthew, et al.
Published: (2024)
by: Ashman, Matthew, et al.
Published: (2024)
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data
by: Ashman, Matthew, et al.
Published: (2024)
by: Ashman, Matthew, et al.
Published: (2024)
Approximately Equivariant Neural Processes
by: Ashman, Matthew, et al.
Published: (2024)
by: Ashman, Matthew, et al.
Published: (2024)
Position: The Future of Bayesian Prediction Is Prior-Fitted
by: Müller, Samuel, et al.
Published: (2025)
by: Müller, Samuel, et al.
Published: (2025)
Orthogonal Finetuning Made Scalable
by: Qiu, Zeju, et al.
Published: (2025)
by: Qiu, Zeju, et al.
Published: (2025)
Does TabPFN Understand Causal Structures?
by: Swelam, Omar, et al.
Published: (2025)
by: Swelam, Omar, et al.
Published: (2025)
SynDaCaTE: A Synthetic Dataset For Evaluating Part-Whole Hierarchical Inference
by: Levi, Jake, et al.
Published: (2025)
by: Levi, Jake, et al.
Published: (2025)
The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning
by: Cui, Shaobo, et al.
Published: (2024)
by: Cui, Shaobo, et al.
Published: (2024)
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes
by: Robertson, Jake, et al.
Published: (2024)
by: Robertson, Jake, et al.
Published: (2024)
FairPFN: Transformers Can do Counterfactual Fairness
by: Robertson, Jake, et al.
Published: (2024)
by: Robertson, Jake, et al.
Published: (2024)
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)
by: Rajendran, Goutham, et al.
Published: (2024)
Elements of Causal Inference
by: Peters, Jonas, et al.
Published: (2019)
by: Peters, Jonas, et al.
Published: (2019)
Causal Modeling with Stationary Diffusions
by: Lorch, Lars, et al.
Published: (2023)
by: Lorch, Lars, et al.
Published: (2023)
Targeted Reduction of Causal Models
by: Kekić, Armin, et al.
Published: (2023)
by: Kekić, Armin, et al.
Published: (2023)
Products, Abstractions and Inclusions of Causal Spaces
by: Buchholz, Simon, et al.
Published: (2024)
by: Buchholz, Simon, et al.
Published: (2024)
Causal Responsibility Attribution for Human-AI Collaboration
by: Qi, Yahang, et al.
Published: (2024)
by: Qi, Yahang, et al.
Published: (2024)
Causality can systematically address the monsters under the bench(marks)
by: Leeb, Felix, et al.
Published: (2025)
by: Leeb, Felix, et al.
Published: (2025)
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
by: Binkyte, Ruta, et al.
Published: (2025)
by: Binkyte, Ruta, et al.
Published: (2025)
Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
by: Thielmann, Anton, et al.
Published: (2025)
by: Thielmann, Anton, et al.
Published: (2025)
Neural Additive Image Model: Interpretation through Interpolation
by: Reuter, Arik, et al.
Published: (2024)
by: Reuter, Arik, et al.
Published: (2024)
Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
by: Bühler, Magnus, et al.
Published: (2026)
by: Bühler, Magnus, et al.
Published: (2026)
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
by: Guo, Siyuan, et al.
Published: (2022)
by: Guo, Siyuan, et al.
Published: (2022)
SPARTAN: A Sparse Transformer World Model Attending to What Matters
by: Lei, Anson, et al.
Published: (2024)
by: Lei, Anson, et al.
Published: (2024)
Large Language Models Must Be Taught to Know What They Don't Know
by: Kapoor, Sanyam, et al.
Published: (2024)
by: Kapoor, Sanyam, et al.
Published: (2024)
A Measure-Theoretic Axiomatisation of Causality
by: Park, Junhyung, et al.
Published: (2023)
by: Park, Junhyung, et al.
Published: (2023)
Visually Dehallucinative Instruction Generation: Know What You Don't Know
by: Cha, Sungguk, et al.
Published: (2024)
by: Cha, Sungguk, et al.
Published: (2024)
Do You Know What Your Mission Is?
by: Balas, Janet L.
Published: (2007)
by: Balas, Janet L.
Published: (2007)
Standardizing Structural Causal Models
by: Ormaniec, Weronika, et al.
Published: (2024)
by: Ormaniec, Weronika, et al.
Published: (2024)
Causal vs. Anticausal merging of predictors
by: Mejia, Sergio Hernan Garrido, et al.
Published: (2025)
by: Mejia, Sergio Hernan Garrido, et al.
Published: (2025)
Know What You Know: Metacognitive Entropy Calibration for Verifiable RL Reasoning
by: Zhao, Qiannian, et al.
Published: (2026)
by: Zhao, Qiannian, et al.
Published: (2026)
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025)
by: Park, Young-Jin, et al.
Published: (2025)
Similar Items
-
Do-PFN: In-Context Learning for Causal Effect Estimation
by: Robertson, Jake, et al.
Published: (2025) -
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
by: Dhir, Anish, et al.
Published: (2025) -
Bivariate Causal Discovery using Bayesian Model Selection
by: Dhir, Anish, et al.
Published: (2023) -
A Meta-Learning Approach to Bayesian Causal Discovery
by: Dhir, Anish, et al.
Published: (2024) -
The relative value of interventional and observational samples in Bayesian Causal Linear Gaussian Models
by: Lungu, Valentinian, et al.
Published: (2026)