File size: 2,366 Bytes
318c59b 3477a84 318c59b 3477a84 318c59b 3477a84 318c59b 3477a84 318c59b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | ---
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}
}
```
|