CSE472-blanket-challenge/SCM3K

SCM3K

Benchmark dataset for:

The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction Shu Wan, Abhinav Gorantla, Huan Liu, K. Selcuk Candan

3 450 tabular prediction tasks sampled from random structural causal models (SCMs), totalling 3.45M records (1 000 samples per task). Each task ships with the ground-truth Markov boundary of the target node, so you can evaluate feature selection and prediction under known causal structure. Nine feature-count levels from 40 to 1 000.

Splits

One HF split per feature count F. No predefined train/test partition — use HF slice syntax (e.g. split="f200[:80%]").

Split	F	num_nodes	DAG density	MB-ratio band	Tasks
f40	40	41	ER [0.2, 0.4]	[0.10, 0.90]	300
f60	60	61	ER [0.2, 0.4]	[0.10, 0.90]	300
f80	80	81	ER [0.2, 0.4]	[0.10, 0.90]	300
f100	100	101	ER [0.2, 0.4]	[0.10, 0.90]	300
f200	200	201	ER [0.01, 0.02, 0.04]	[0.05, 0.95]	450
f400	400	401	ER [0.01, 0.02, 0.04]	[0.05, 0.95]	450
f600	600	601	ER [0.01, 0.02, 0.04]	[0.05, 0.95]	450
f800	800	801	ER [0.01, 0.02, 0.04]	[0.05, 0.95]	450
f1000	1000	1001	ER [0.01, 0.02, 0.04]	[0.05, 0.95]	450
				Total	3 450

Row schema

Each row is one prediction task.

Field	Type	What it stores
task_id	string	unique task identifier
X	list<list<f32>>	feature matrix, 1 000 x F
y	list<f32>	target vector, length 1 000
n_features	int	number of features (= F)
mb_mask	list<bool>	true Markov boundary mask over features
mb_ratio	float	fraction of features in the Markov boundary
density	float	edge density of the generating DAG
dag	struct	dag_id, num_nodes, edge_list
scm	struct	scm_type, noise_model, pnl, coeff_range, noise_std
target	struct	target_node, parent_mask, child_mask, spouse_mask
meta	struct	n_samples, seed

How the data was generated

DAGs: Erdos-Renyi, 5 graphs per (num_nodes, density) pair, seed 42.

SCMs: six families — LINEAR_GAUSSIAN, LINEAR_NONGAUSSIAN, NL_ANM_GAUSSIAN, NL_ANM_NONGAUSSIAN, PNL, HETEROSKEDASTIC. Each DAG gets 5 SCM instantiations with n_samples=1000, coeff_range=1.0, noise_std=0.5.

Quick start

from datasets import load_dataset

ds = load_dataset("CSE472-blanket-challenge/SCM3K", split="f200")
task = ds[0]

X  = task["X"]          # 1000 x 200
y  = task["y"]          # 1000
mb = task["mb_mask"]    # ground-truth Markov boundary

Citation

@article{wan2026gbu,
  title  = {The Good, the Bad, and the Ugly of Markov Boundary
            for Tabular Prediction},
  author = {Wan, Shu and Gorantla, Abhinav and Liu, Huan
            and Candan, K. Sel{\c{c}}uk},
  year   = {2026},
}