Upload README.md with huggingface_hub

README.md

@@ -26,24 +26,30 @@ This repository contains test data for CauScale, a neural architecture for causa
 
 ## Data Format
 
-Each dataset instance consists of:
+### Synthetic Data
+
+Generated using linear, neural network, and polynomial structural equation models on Erdős–Rényi (ER) and Scale-Free (SF) graphs.
 
-- `DAG{i}.npy` — ground-truth adjacency matrix `(d, d)`, boolean, where entry `(i, j) = True` means i → j
+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)
 
-### 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
+**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).
 
-- **Test**: Nodes 100–200, ER graphs, 2 expected degrees (2, 4), 5 graphs each, 20000 interventional points
+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