Do-PFN: In-Context Learning for Causal Effect Estimation
Fuente:
arXiv
Saved in:
| Main Authors: | Robertson, Jake, Reuter, Arik, Guo, Siyuan, Hollmann, Noah, Hutter, Frank, Schölkopf, Bernhard |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FairPFN: A Tabular Foundation Model for Causal Fairness
by: Robertson, Jake, et al.
Published: (2025)
by: Robertson, Jake, et al.
Published: (2025)
FairPFN: Transformers Can do Counterfactual Fairness
by: Robertson, Jake, et al.
Published: (2024)
by: Robertson, Jake, 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)
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
by: Helli, Kai, et al.
Published: (2024)
by: Helli, Kai, et al.
Published: (2024)
Bayes' Power for Explaining In-Context Learning Generalizations
by: Müller, Samuel, et al.
Published: (2024)
by: Müller, Samuel, et al.
Published: (2024)
Does TabPFN Understand Causal Structures?
by: Swelam, Omar, et al.
Published: (2025)
by: Swelam, Omar, et al.
Published: (2025)
Use What You Know: Causal Foundation Models with Partial Graphs
by: Reuter, Arik, et al.
Published: (2026)
by: Reuter, Arik, et al.
Published: (2026)
Do Finetti: On Causal Effects for Exchangeable Data
by: Guo, Siyuan, et al.
Published: (2024)
by: Guo, Siyuan, et al.
Published: (2024)
Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
by: Garg, Anurag, et al.
Published: (2025)
by: Garg, Anurag, et al.
Published: (2025)
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
by: Balazadeh, Vahid, et al.
Published: (2025)
by: Balazadeh, Vahid, et al.
Published: (2025)
Transformers Can Do Bayesian Inference
by: Müller, Samuel, et al.
Published: (2021)
by: Müller, Samuel, et al.
Published: (2021)
Physics of Learning: A Lagrangian perspective to different learning paradigms
by: Guo, Siyuan, et al.
Published: (2025)
by: Guo, Siyuan, et al.
Published: (2025)
TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
by: Grinsztajn, Léo, et al.
Published: (2025)
by: Grinsztajn, Léo, et al.
Published: (2025)
nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN
by: Pfefferle, Alexander, et al.
Published: (2025)
by: Pfefferle, Alexander, et al.
Published: (2025)
TabPFN-3: Technical Report
by: Grinsztajn, Léo, et al.
Published: (2026)
by: Grinsztajn, Léo, 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)
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
by: Reizinger, Patrik, et al.
Published: (2024)
by: Reizinger, Patrik, et al.
Published: (2024)
MapPFN: Learning Causal Perturbation Maps in Context
by: Sextro, Marvin, et al.
Published: (2026)
by: Sextro, Marvin, et al.
Published: (2026)
Out-of-Variable Generalization for Discriminative Models
by: Guo, Siyuan, et al.
Published: (2023)
by: Guo, Siyuan, et al.
Published: (2023)
Can Transformers Learn Full Bayesian Inference in Context?
by: Reuter, Arik, et al.
Published: (2025)
by: Reuter, Arik, et al.
Published: (2025)
EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks
by: Arbel, Michael, et al.
Published: (2025)
by: Arbel, Michael, 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)
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)
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)
From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
by: Hoo, Shi Bin, et al.
Published: (2025)
by: Hoo, Shi Bin, et al.
Published: (2025)
Counterfactual reasoning: an analysis of in-context emergence
by: Miller, Moritz, et al.
Published: (2025)
by: Miller, Moritz, et al.
Published: (2025)
Robustness of Nonlinear Representation Learning
by: Buchholz, Simon, et al.
Published: (2025)
by: Buchholz, Simon, et al.
Published: (2025)
In-Context Data Distillation with TabPFN
by: Ma, Junwei, et al.
Published: (2024)
by: Ma, Junwei, et al.
Published: (2024)
CausalCite: A Causal Formulation of Paper Citations
by: Kumar, Ishan, et al.
Published: (2023)
by: Kumar, Ishan, et al.
Published: (2023)
Causality can systematically address the monsters under the bench(marks)
by: Leeb, Felix, et al.
Published: (2025)
by: Leeb, Felix, et al.
Published: (2025)
TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
by: Moroshan, Vladyslav, et al.
Published: (2025)
by: Moroshan, Vladyslav, et al.
Published: (2025)
On the Emergence and Test-Time Use of Structural Information in Large Language Models
by: Chen, Michelle Chao, et al.
Published: (2026)
by: Chen, Michelle Chao, et al.
Published: (2026)
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
by: Reizinger, Patrik, et al.
Published: (2025)
by: Reizinger, Patrik, et al.
Published: (2025)
Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
by: Kekić, Armin, et al.
Published: (2025)
by: Kekić, Armin, et al.
Published: (2025)
Early Stopping Tabular In-Context Learning
by: Küken, Jaris, et al.
Published: (2025)
by: Küken, Jaris, et al.
Published: (2025)
Standardizing Structural Causal Models
by: Ormaniec, Weronika, et al.
Published: (2024)
by: Ormaniec, Weronika, et al.
Published: (2024)
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
by: Kekić, Armin, et al.
Published: (2025)
by: Kekić, Armin, et al.
Published: (2025)
Online Learning and Unlearning
by: Hu, Yaxi, et al.
Published: (2025)
by: Hu, Yaxi, et al.
Published: (2025)
Generative Intervention Models for Causal Perturbation Modeling
by: Schneider, Nora, et al.
Published: (2024)
by: Schneider, Nora, et al.
Published: (2024)
Similar Items
-
FairPFN: A Tabular Foundation Model for Causal Fairness
by: Robertson, Jake, et al.
Published: (2025) -
FairPFN: Transformers Can do Counterfactual Fairness
by: Robertson, Jake, et al.
Published: (2024) -
Position: The Future of Bayesian Prediction Is Prior-Fitted
by: Müller, Samuel, et al.
Published: (2025) -
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
by: Helli, Kai, et al.
Published: (2024) -
Bayes' Power for Explaining In-Context Learning Generalizations
by: Müller, Samuel, et al.
Published: (2024)