| --- |
| 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} |
| } |
| ``` |
|
|
