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        <title>Contextual Causality with Large Language Models: A Survey</title>
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        <namePart type="given">Yiheng</namePart>
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    <abstract>Understanding contextual causality is critical for large language models (LLMs), as it enables them to accurately identify causal relations in specific situations and support more reliable decision-making. Despite its significance, a systematic exploration of contextual causality with LLMs is still lacking. To fill this gap, we present a comprehensive survey on this topic. In this survey, we first propose a taxonomy of contextual causality, consisting of semantic, intervention, and counterfactual causality, and characterize each category by its core causal question, required model capabilities, representative tasks, and practical uses in causality analysis. We then analyze existing studies and discuss their key limitations. Finally, we examine the gaps between current benchmarks and real-world needs and outline promising directions for future research. Our goal is to clarify the research landscape of contextual causality with LLMs, emphasize its importance, and highlight promising future directions.</abstract>
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%0 Conference Proceedings
%T Contextual Causality with Large Language Models: A Survey
%A Zhao, Yiheng
%A Yan, Jun
%A Hu, Chengming
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhao-etal-2026-contextual
%X Understanding contextual causality is critical for large language models (LLMs), as it enables them to accurately identify causal relations in specific situations and support more reliable decision-making. Despite its significance, a systematic exploration of contextual causality with LLMs is still lacking. To fill this gap, we present a comprehensive survey on this topic. In this survey, we first propose a taxonomy of contextual causality, consisting of semantic, intervention, and counterfactual causality, and characterize each category by its core causal question, required model capabilities, representative tasks, and practical uses in causality analysis. We then analyze existing studies and discuss their key limitations. Finally, we examine the gaps between current benchmarks and real-world needs and outline promising directions for future research. Our goal is to clarify the research landscape of contextual causality with LLMs, emphasize its importance, and highlight promising future directions.
%U https://aclanthology.org/2026.findings-emnlp.493/
%P 10333-10348

Markdown (Informal)

[Contextual Causality with Large Language Models: A Survey](https://aclanthology.org/2026.findings-emnlp.493/) (Zhao et al., Findings 2026)

• Contextual Causality with Large Language Models: A Survey (Zhao et al., Findings 2026)

ACL

• Yiheng Zhao, Jun Yan, and Chengming Hu. 2026. Contextual Causality with Large Language Models: A Survey. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 10333–10348, Budapest, Hungary. Association for Computational Linguistics.