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MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models

Paper · 2026 · Peng Wang, Yuxiong Yan, Xiao Ding et al.

Summary

Most existing causal benchmarks for LLMs use everyday commonsense scenarios with short, single-hop cause–effect links. A model can often answer these by recalling familiar facts rather than reasoning about causation. MDC-Bench addresses this with questions built from academic papers in seven disciplines, where pre-training coverage is thinner and memorized associations help less. DeepSeek served as the construction tool. It extracted long causal chains from paper introductions, preprocessed with SURD, and generated distractor options of decreasing plausibility. Compressed background summaries supply domain basics without revealing the target chain. Tasks follow Pearl's causal ladder across three levels: understanding, intervention and counterfactual. The questions cover chain, fork, collider and hybrid structures, and compound operations add difficulty. Evaluation of open- and closed-source LLMs shows some causal competence, which grows modestly with model size. Even top models still struggle with deep causal understanding and handle chain structures better than other structures. The benchmark offers a more discriminative, domain-aware test of causal ability for high-reliability applications.

Key points

  • The tasks are arranged in three layers. The understanding layer has forward and reverse causal reasoning. The intervention layer has causal intervention and causal decision tasks, each in single and compound variants. The counterfactual layer has counterfactual reasoning under restrictive conditions.
  • Source papers come from seven disciplines:
    • Chemistry & Material Sciences
    • Engineering
    • Medicine & Pharmacology
    • Public Health & Healthcare
    • Arts & Humanities
    • Business, Economics & Management
    • Social Sciences
  • Causal structures include basic chain, fork and collider patterns plus hybrids: chain-fork, chain-collider, fork-collider and chain-fork-collider.
  • A hard mode shifts most samples to moderate difficulty, which separates strong models from average ones more clearly.
  • The authors note two limitations: links between distant head and tail events can be weak, and some distractors are too implausible to confuse a model.

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

🎓Harbin Institute of TechnologyHarbin · China158 works · 28,841 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksEvaluation & benchmarks · 1,220 works

Cite

Cite as Peng Wang, Yuxiong Yan, Xiao Ding, Kai Xiong, Bibo Cai, Chao Peng, Yutai Hou, Dandan Tu, Bing Qin and Ting Liu. 2026. MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1409

@inproceedings{wang2026mdc,
  title = {{MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models}},
  author = {Peng Wang, Yuxiong Yan, Xiao Ding, Kai Xiong, Bibo Cai, Chao Peng, Yutai Hou, Dandan Tu, Bing Qin and Ting Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1409},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1409}
}

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