---
title: "CRAFT — paper by Ates et al. (2022) — summary and ideas"
description: "The paper tests whether vision-language models can reason about causes and effects… 47% accuracy. 51 references, cited by 8 papers here."
authors: ["Tayfun Ates", "M. Ateşoğlu", "Çağatay Yiğit", "Ilker Kesen", "Mert Kobas", "Erkut Erdem", "Aykut Erdem", "Tilbe Goksun", "Deniz Yuret"]
year: 2022
venue: "Findings of the Association for Computational Linguistics: ACL 2022"
source: "https://aclanthology.org/2022.findings-acl.205/"
doi: "10.18653/v1/2022.findings-acl.205"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ates-2022-craft-benchmark-causal-reasoning-about/"
---
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# CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions

**Authors:** Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, [Erkut Erdem](https://md.research.elamur.ai/free/en/authors/erkut-erdem/), [Aykut Erdem](https://md.research.elamur.ai/free/en/authors/aykut-erdem/), Tilbe Goksun and [Deniz Yuret](https://md.research.elamur.ai/free/en/authors/deniz-yuret/) · **Year:** 2022 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2022](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2022](https://md.research.elamur.ai/free/en/venues/findings/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.findings-acl.205/) · [PDF](https://aclanthology.org/2022.findings-acl.205.pdf) · [DOI 10.18653/v1/2022.findings-acl.205](https://doi.org/10.18653/v1/2022.findings-acl.205) · [OpenAlex](https://openalex.org/W3113227982)

**Citations:** 9 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/ates-2022-craft-benchmark-causal-reasoning-about/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper tests whether vision-language models can reason about causes and effects in physical interactions between objects. The authors created CRAFT, a dataset for video question answering. It consists of synthetic 2D videos simulated in the Box2D physics engine. Questions about the videos are generated automatically from templates, and heuristics remove obvious biases in the data. Besides the descriptive and counterfactual questions known from CLEVRER, the authors added a new category of causal questions based on Force Dynamics Theory from cognitive linguistics. In these questions, the model must determine whether one object caused (cause), enabled (enable) or prevented (prevent) an event involving another object. Evaluation has an easy and a hard setting. In the hard setting, the test scenes have layouts that the model did not see during training. The evaluated models include text-only models, single-frame models and video models, among them MAC, TVQA and G-SWM. In the hard setting, none exceeded roughly 47% accuracy, while humans reached roughly 77%. The benchmark clearly reveals a gap in the physical and causal reasoning of current models.

## Key points
- **Data size.** About 58K video–question pairs based on 10K videos from 20 different scene layouts. Each video lasts 10 s and has a resolution of 256×256. The data is split into train/val/test in a 60:20:20 ratio by video. The abstract states 58K, while the data section states about 57K.
- **Structure.** 48 task types in three categories: Descriptive, Counterfactual and Causal (cause / enable / prevent). A scene has 7 static elements, including a ramp, a platform, a button, a basket and walls. For counterfactual answers, additional variants of each video are generated in which each object is removed in turn.
- **Model results.** In the easy setting, the best model was LSTM-CNN-V with 53.01%; in the hard setting, MAC-V with 47.31%. The text-only LSTM without video scores about 44.5%, so the visual models gain little. Models struggle most with descriptive questions: 23–48%.
- **Humans and oracles.** The study involved 101 adults, of whom 56 were included in the final analysis. Overall human accuracy is 76.60%, and the gap with the models in the hard setting exceeds 29 percentage points. The oracle model BERT-D reaches 84.90–86.20%, and on descriptive questions it even slightly outperforms humans.
- **Availability.** The data and code are published on the project website: sites.google.com/view/craft-benchmark. As next steps, the authors propose neuro-symbolic models (e.g., NS-DR) and object-centric methods with pre-training.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Koç University](https://md.research.elamur.ai/free/en/universities/koc-university/) — University, Istanbul, Türkiye · 4 works · 431 citations
- [Hacettepe University](https://md.research.elamur.ai/free/en/universities/hacettepe-university/) — University, Ankara, Türkiye · 3 works · 25 citations

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🧭 Topics (2): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,948 works · [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · 1,394 works

## Cite

Cite as: Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun and Deniz Yuret. 2022. CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions. Findings of the Association for Computational Linguistics: ACL 2022.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ates2022craft,
  title = {{CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions}},
  author = {Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun and Deniz Yuret},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2022},
  year = {2022},
  doi = {10.18653/v1/2022.findings-acl.205},
  url = {https://doi.org/10.18653/v1/2022.findings-acl.205}
}
```

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## References

51 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 3 November 2026*