---
license: mit
library_name: pytorch
tags:
- causal-discovery
- tabular
- directed-acyclic-graphs
- zero-shot
- pytorch
- arxiv:2605.07204
---

# ArrowFM Base

Arrow is a zero-shot foundation model for causal discovery from observational tabular data.

Given a numeric dataset, Arrow predicts edge-existence probabilities and node-order scores, then decodes them into a directed acyclic graph (DAG).

- Paper: [Arrow: A Foundation Model for Causal Discovery](https://arxiv.org/abs/2605.07204)
- Code: [github.com/ryan-thompson/arrowfm](https://github.com/ryan-thompson/arrowfm)

## Usage

Install the Python package:

```bash
python3 -m pip install "git+https://github.com/ryan-thompson/arrowfm.git"
```

Run inference:

```python
import torch
from arrowfm import ArrowPredictor

predictor = ArrowPredictor()
x = torch.randn(100, 10)
adj = predictor.predict_adjacency(x)
```

`x` should have shape `(n, p)` for one dataset or `(batch, n, p)` for batched input. The returned `adj` is a boolean adjacency matrix where `adj[j, k]` indicates a directed edge from variable `j` to variable `k`.

To also return edge probabilities:

```python
adj, p_hat = predictor.predict_adjacency(x, return_p_hat = True)
```

## Citation

```bibtex
@misc{thompson2026arrow,
  title         = {Arrow: A Foundation Model for Causal Discovery},
  author        = {Thompson, Ryan and Zhao, He and Steinberg, Daniel M. and Bonilla, Edwin V.},
  year          = {2026},
  eprint        = {2605.07204},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2605.07204}
}
```