---
license: cc-by-nc-4.0
task_categories:
- text-classification
- token-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
tags:
- causality
pretty_name: PolitiCause
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
            '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 PolitiCause into hf datasets. Please find the original dataset
> [here](https://github.com/pgarco/PolitiCAUSE). The original release only ships sentence-level
> causal/noncausal labels (`train.csv`/`val.csv`/`test.csv`, used for `causality detection` here) plus a
> SEPARATE multi-annotator span file (`span_annotations.csv`, 2-9 independent passes per sentence) that the
> source paper itself never reduces to one gold span set (it reports no extraction results). The `causal
> candidate extraction`/`causality identification` configs here are therefore DERIVED, not verbatim: for each
> gold-causal sentence, one canonical cause/effect pair is picked via a documented reconciliation policy
> (prefer a balanced, non-empty Cause+Effect annotator pass, tie-broken by that annotator's own confidence;
> drop the sentence if no annotator pass qualifies at all). This drops ~9% of gold-causal sentences and loses
> an extra span on another ~9% of the kept ones — see `conversion_script.py`'s module docstring for the full
> policy and exact counts before relying on these two configs.

## Dataset Description

- **Repository:** https://github.com/pgarco/PolitiCAUSE
- **Paper:** [PolitiCause: An Annotation Scheme and Corpus for Causality in Political Texts](https://aclanthology.org/2024.lrec-main.1124/)

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

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

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

# Citations
```bib
@inproceedings{corral:2024,
  title = {{{PolitiCause}}: {{An Annotation Scheme}} and {{Corpus}} for {{Causality}} in {{Political Texts}}},
  shorttitle = {{{PolitiCause}}},
  booktitle = {Proceedings of the 2024 {{Joint International Conference}} on {{Computational Linguistics}}, {{Language Resources}} and {{Evaluation}}, {{LREC}}/{{COLING}} 2024, 20-25 {{May}}, 2024, {{Torino}}, {{Italy}}},
  author = {Corral, Paulina Garcia and B{\'e}chara, Hanna and Zhang, Ran and Jankin, Slava},
  editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, V{\'e}ronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
  year = 2024,
  pages = {12836--12845},
  publisher = {{ELRA and ICCL}},
  url = {https://aclanthology.org/2024.lrec-main.1124},
  urldate = {2025-02-08}
}
```