README.md

---
license: unknown
task_categories:
- text-classification
- token-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
tags:
- causality
- biomedical
pretty_name: PubMedCausal
configs:
- config_name: causality detection
  data_files:
  - split: train
    path: causality-detection/train.parquet
  - split: test
    path: causality-detection/test.parquet
  features:
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': uncausal
          '1': causal
- config_name: causal candidate extraction
  data_files:
  - split: train
    path: causal-candidate-extraction/train.parquet
  - split: test
    path: causal-candidate-extraction/test.parquet
  features:
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: entity
    sequence:
      sequence: int32
- config_name: causality identification
  data_files:
  - split: train
    path: causality-identification/train.parquet
  - split: test
    path: causality-identification/test.parquet
  features:
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: relations
    list:
    - name: relationship
      dtype:
        class_label:
          names:
            '0': no-rel  # Does not really make sense but exists to have the same labels as the classification task
            '1': causal
    - name: first
      dtype: string
    - name: second
      dtype: string
train-eval-index:
- config: causality detection
  task: text-classification
  task_id: text_classification
  splits:
    train_split: train
    eval_split: test
  col_mapping:
    text: text
    label: label
  metrics:
  - type: accuracy
  - type: precision
  - type: recall
  - type: f1
- config: causal candidate extraction
  task: token-classification
  task_id: token_classification
  splits:
    train_split: train
    eval_split: test
  metrics:
  - type: accuracy
  - type: precision
  - type: recall
  - type: f1
- config: causality identification
  task: text-classification
  task_id: text_classification
  splits:
    train_split: train
    eval_split: test
  metrics:
  - type: accuracy
  - type: precision
  - type: recall
  - type: f1
---

> [!NOTE]  
> This repository integrates the PubMedCausal causal relation extraction corpus into hf datasets.
> Please find the original dataset [here](https://github.com/josiahpaul07/PubMedCausal_Exp). Please
> see the [citations](#citations) at the end of this README.


## Dataset Description

- **Repository:** https://github.com/josiahpaul07/PubMedCausal_Exp
- **Paper:** Adewole et al. (2025). *PubMedCausal: A Biomedical Causal Relation Extraction Corpus.* [arXiv:2605.28363](https://arxiv.org/abs/2605.28363)

PubMedCausal is a biomedical causal relation extraction corpus built from PubMed abstracts.
Pairs are typed along two dimensions:

- **Causality:** Explicit / Implicit
- **Sententiality:** Intra-sentential / Inter-sentential

There are no countercausal annotations — all relations map to `Relation.Procausal`.

## Statistics

| Task | Train | Test |
|------|-------|------|
| Causality detection (sentences) | 15,000 (1,972 causal) | 15,000 (1,973 causal) |
| Causal candidate extraction | 1,972 | 1,973 |
| Causality identification | 1,529 | 1,528 |

# Usage
## Causality Detection
```py
from datasets import load_dataset
dataset = load_dataset("webis/PubMedCausal", "causality detection")
```

## Causal Candidate Extraction
```py
from datasets import load_dataset
dataset = load_dataset("webis/PubMedCausal", "causal candidate extraction")
```

## Causality Identification
```py
from datasets import load_dataset
dataset = load_dataset("webis/PubMedCausal", "causality identification")
```

## Conversion

Data is fetched directly from GitHub at conversion time; no local download is needed.

```bash
python conversion_script.py
```

This produces one parquet per split for each of the three tasks under this directory:

```
causality-detection/
causal-candidate-extraction/
causality-identification/
```

## Known limitation — unresolvable spans in the identification task

PubMedCausal sometimes records the **canonical / normalised form** of a span rather
than the exact surface text from the sentence. For example:

```
sentence: "...thus precluding practice and placebo effects..."
effect annotation: "precludes practice"   ← lemmatised, does not match
```

Because the identification task requires inserting `<e1>…</e1>` markers at exact
character positions, pairs where either span cannot be located verbatim are
**skipped** during conversion. The conversion script prints a warning for each
skipped pair and a summary count at the end.

This affects **1,078 train pairs and 997 test pairs** (roughly 28% of intra-sentential
pairs), predominantly Implicit-causality annotations where the annotator wrote the
inferred proposition rather than the surface string. The detection and extraction
tasks are unaffected.

# Citations

The PubMedCausal paper by Adewole et al., 2025:
```bib
@article{kunle-john:2026,
  title = {{{PubMedCausal}}: {{A Span-Level Annotated Corpus}} for {{Causal Relation Extraction}} in {{Biomedical Text}}},
  shorttitle = {{{PubMedCausal}}},
  author = {{Kunle-John}, Ifeoluwa and Paul, Josiah and Agbaakin, Oluwatosin and Aina, Peter and Odezuligbo, Ikenna and Anuyah, Sydney},
  year = 2026,
  journal = {CoRR},
  volume = {abs/2605.28363},
  eprint = {2605.28363},
  doi = {10.48550/ARXIV.2605.28363},
  url = {https://doi.org/10.48550/arXiv.2605.28363},
  urldate = {2026-07-01},
  archiveprefix = {arXiv}
}
```