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license: apache-2.0
tags:
- causal-discovery
- causal-inference
- graph-ml
pretty_name: CauScale
---
# CauScale
Pretrained checkpoints for **[CauScale: Neural Causal Discovery at Scale](https://arxiv.org/abs/2602.08629)** (ICML 2026).
## Checkpoints
| File | Trained on | AUPRC |
|------|-----------|-------|
| `synthetic/auprc=0.905_migrated.ckpt` | Synthetic data (10–500 nodes) | 0.905 |
| `sergio/auprc=0.703_migrated.ckpt` | SERGIO gene expression data (10–200 nodes) | 0.703 |
## Usage
Download and place under `checkpoints/`:
```python
from huggingface_hub import hf_hub_download
hf_hub_download(
repo_id="OpenCausaLab/causcale-model",
filename="synthetic/auprc=0.905_migrated.ckpt",
repo_type="model",
local_dir="checkpoints",
)
hf_hub_download(
repo_id="OpenCausaLab/causcale-model",
filename="sergio/auprc=0.703_migrated.ckpt",
repo_type="model",
local_dir="checkpoints",
)
```
Then run inference:
```bash
bash bash/inference-synthetic.sh # synthetic data
bash bash/inference-sergio.sh # SERGIO gene expression data
```
See the [CauScale code repository](https://github.com/OpenCausaLab/CauScale) for full instructions.
## Citation
```bibtex
@article{peng2026causcale,
title={CauScale: Neural Causal Discovery at Scale},
author={Peng, Bo and Chen, Sirui and Tian, Jiaguo and Qiao, Yu and Lu, Chaochao},
journal={arXiv preprint arXiv:2602.08629},
year={2026}
}
```
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