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README.md

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+---
+license: apache-2.0
+task_categories:
+  - graph-ml
+tags:
+  - causal-discovery
+  - causal-inference
+  - synthetic
+  - gene-regulatory-network
+pretty_name: CauScale Dataset
+---
+
+# CauScale Dataset
+
+Dataset for **[CauScale: Neural Causal Discovery at Scale](https://arxiv.org/abs/2602.08629)** (ICML 2026).
+
+## Overview
+
+This repository contains test data for CauScale, a neural architecture for causal graph discovery that scales to graphs with up to 1000 nodes.
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `data_test.zip` | Test data for synthetic and SERGIO benchmarks |
+
+## Data Format
+
+Each dataset instance consists of:
+
+- `DAG{i}.npy` — ground-truth adjacency matrix `(d, d)`, boolean, where entry `(i, j) = True` means i → j
+- `data_interv{i}.npy` — concatenated observational and interventional data `(N, d)`
+- `intervention{i}.csv` — intervened node index per row (empty = observational)
+- `regime{i}.csv` — environment index per row (0 = observational, 1..K = interventional)
+
+### Synthetic Data
+
+Generated using linear, neural network, and polynomial structural equation models on Erdős–Rényi (ER) and Scale-Free (SF) graphs.
+
+- **Test**: 10–1000 nodes graph-sizes, 5 DAGs each, 1000 data points per DAG
+
+### SERGIO Gene Expression Data
+
+Simulated single-cell gene expression data using the [SERGIO simulator](https://github.com/PayamDiba/SERGIO).
+
+- **Test**: Nodes 100–200, ER graphs, 2 expected degrees (2, 4), 5 graphs each, 20000 interventional points
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="OpenCausaLab/causcale-data",
+    filename="data_test.zip",
+    repo_type="dataset",
+)
+```
+
+See the [CauScale code repository](https://github.com/OpenCausaLab/CauScale) for full training and inference 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}
+}
+```