README.md

---
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

### Synthetic Data

Generated using linear, neural network, and polynomial structural equation models on Erdős–Rényi (ER) and Scale-Free (SF) graphs.

Each instance folder contains:
- `DAG{i}.npy` — ground-truth adjacency matrix `(d, d)`, boolean, 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)

**Test**: 10–1000 nodes, 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).

Each instance folder contains:
- `DAG.npy` — ground-truth adjacency matrix `(d, d)`, boolean
- `data_intv.npy` — interventional data `(N_int, d)`
- `intv.npy` — intervention mask `(N_int, d)`, boolean
- `data.npy` / `clean.npy` / `dropout.npy` — observational data variants `(150, d)`
- `info.json` — metadata

**Test**: Nodes 100–200, ER graphs, expected degrees 2 and 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}
}
```