Link dataset to paper and update metadata

README.md

@@ -1,4 +1,13 @@
 ---
+license: mit
+size_categories:
+- 1M<n<10M
+task_categories:
+- tabular-regression
+tags:
+- causal-discovery
+- markov-boundary
+- causal-inference
 dataset_info:
   features:
   - name: task_id
@@ -6,7 +15,7 @@ dataset_info:
   - name: X
     list:
       list: float32
-  - name: 'y'
+  - name: y
     list: float32
   - name: n_features
     dtype: int32
@@ -104,25 +113,20 @@ configs:
     path: data/f60-*
   - split: f80
     path: data/f80-*
-license: mit
-task_categories:
-- tabular-regression
-size_categories:
-- 100K<n<1M
 ---
 
 ## SCM3K
 
-Benchmark dataset for:
+Benchmark dataset for the paper:
 
-> **The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction**
-> Shu Wan, Abhinav Gorantla, Huan Liu, K. Selcuk Candan
+> **[The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction](https://huggingface.co/papers/2605.29411)**
+> [Shu Wan](https://huggingface.co/Shuwan), Abhinav Gorantla, Huan Liu, K. Selçuk Candan
 
-3 450 tabular prediction tasks sampled from random structural causal
-models (SCMs), totalling 3.45M records (1 000 samples per task).
+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.
+known causal structure. Nine feature-count levels from 40 to 1,000.
 
 ### Splits
 
@@ -140,7 +144,7 @@ partition — use HF slice syntax (e.g. `split="f200[:80%]"`).
 | 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** |
+| | | | | **Total** | **3,450** |
 
 ### Row schema
 
@@ -149,8 +153,8 @@ 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 |
+| `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 |