Use What You Know: Causal Foundation Models with Partial Graphs
cs.LG
Submitted: 2026-02-16
Updated: 2026-09-27
Code: https://github.com/ArikReuter/Graphs4CausalFoundationModels
Terminology
Sources
- CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
- Convolutional Networks on Graphs for Learning Molecular Fingerprints
- TabArena: A Living Benchmark for Machine Learning on Tabular Data
- Neural Processes
- TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
- Gaussian Error Linear Units (GELUs)
- TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
- Adam: A Method for Stochastic Optimization
- Foundation Models for Causal Inference via Prior-Data Fitted Networks
- A detailed treatment of Doob's theorem
- Transformers Can Do Bayesian Inference
- RealCause: Realistic Causal Inference Benchmarking
- Scalable Diffusion Models with Transformers
- TabICLv2: A better, faster, scalable, and open tabular foundation model
- Do-PFN: In-Context Learning for Causal Effect Estimation
- ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder
- Estimating individual treatment effect: generalization bounds and algorithms
- GLU Variants Improve Transformer
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
- LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks