README.md

---
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}
}
```