---
title: "Can Large Language Models Infer Causal Relationships from"
description: "The authors build ReCITE, a benchmark of 292 open-access MDPI and PLOS papers that use causal… 46 references, cited by 1 paper here."
authors: ["Ryan Saklad", "Aman Chadha", "Oleg V. Pavlov", "Raha Moraffah"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1003/"
doi: "10.18653/v1/2026.acl-long.1003"
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/saklad-2026-can-large-language-models-infer/"
---
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# Can Large Language Models Infer Causal Relationships from Real-World Text?

**Authors:** Ryan Saklad, [Aman Chadha](https://md.research.elamur.ai/free/en/authors/aman-chadha/), Oleg V. Pavlov and Raha Moraffah · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **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/2026.acl-long.1003/) · [PDF](https://aclanthology.org/2026.acl-long.1003.pdf) · [DOI 10.18653/v1/2026.acl-long.1003](https://doi.org/10.18653/v1/2026.acl-long.1003) · [OpenAlex](https://openalex.org/W7166819299)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most tests of LLM causal reasoning use short synthetic passages where cause and effect are stated outright. This work asks whether models can recover causal structure from long, authentic documents where many links are only implied. The authors build ReCITE, a benchmark of 292 open-access MDPI and PLOS papers that use causal loop diagrams. In each paper, the human-drawn diagram was transcribed by annotators into a list of directed edges, which serves as the ground-truth causal graph. Given the full paper text, a model must produce the whole graph: events as nodes and causal links as edges. Outputs are scored by an LLM-as-a-Judge framework.

Ten state-of-the-art LLMs were tested zero-shot, and all performed weakly. The top average F1 was 0.535. Scores dropped further as causal statements became less explicit, and stayed low even when node names were supplied. This suggests the bottleneck is causal inference rather than spotting entities. The benchmark therefore exposes a concrete gap relevant to document-level reasoning.

## Key points
- **Corpus construction:** a keyword search for "causal loop diagram" returned 646 candidate papers. After manual filtering, 292 remained, each containing a single usable causal graph.
- **Scale and variety:**
  - Graphs have 5–140 nodes and 6–205 edges.
  - Texts are 684–171,141 characters long.
  - About 87.7% of nodes are mentioned in the text.
  - Papers span 17 fields, with Environmental Science and Engineering at roughly 20% each.
- **Annotation quality:** a second annotator re-labelled about 13% of the graphs. Edge-level agreement reached F1 0.984 and Cohen's κ 0.987.
- **Results:** Claude Opus 4.5 was the strongest model overall, but no model did well in any domain. LLM judges correlated strongly with one another (r > 0.78). All experiments cost under $1000.
- **Limitations:**
  - The data is English-only.
  - Models are told the target node count.
  - Evaluation is zero-shot with default settings.
  - All graphs come from system-dynamics-style diagrams.

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

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- [Worcester Polytechnic Institute](https://md.research.elamur.ai/free/en/universities/worcester-polytechnic-institute/) — University, Worcester, United States · 12 works · 380 citations

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## Cite

Cite as: Ryan Saklad, Aman Chadha, Oleg V. Pavlov and Raha Moraffah. 2026. Can Large Language Models Infer Causal Relationships from Real-World Text?. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{saklad2026can,
  title = {{Can Large Language Models Infer Causal Relationships from Real-World Text?}},
  author = {Ryan Saklad, Aman Chadha, Oleg V. Pavlov and Raha Moraffah},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1003},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1003}
}
```

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*Updated 2 November 2026*