| Challenge: | Recent advances in Large Language Models (LLMs) have opened up new avenues for their use beyond standard Natural Language Processing tasks. |
| Approach: | They propose a benchmark to evaluate the capabilities of Large Language Models (LLMs) they use over 700k queries to compare their encoding capabilities. |
| Outcome: | The proposed benchmark compared LLMs on graph-level and node-level queries and open-sourced and closed models. |
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CausalGraphBench: a Benchmark for Evaluating Language Models capabilities of Causal Graph discovery (2025.acl-srw)
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| Challenge: | Recent advances in large language models (LLMs) have expanded their applications into domains not traditionally associated with natural language processing. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to construct Causal Graphs (CGs) they examine various methods for CG discovery and their performance across different graph sizes and complexity levels. |
| Outcome: | The proposed benchmark comprises 35 CGs sourced from publicly available repositories and academic papers. |
Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery (2025.findings-emnlp)
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| Challenge: | Current causal discovery methods rely on pairwise or iterative strategies that fail to capture global dependencies, amplify local biases, and reduce overall accuracy. |
| Approach: | They propose a framework for one-step full causal graph discovery using prompt-based discovery and a data-driven method for settings without metadata. |
| Outcome: | The proposed framework outperforms state-of-the-art models by approximately 40% in edge accuracy on datasets like Asia and Sachs while maintaining strong performance on more complex graphs. |
CLEAR: Can Language Models Really Understand Causal Graphs? (2024.findings-emnlp)
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| Challenge: | Existing language models lack a conceptual framework for understanding causal graphs, but there is still potential for improvement. |
| Approach: | They develop a framework to define causal graph understanding by assessing language models’ behaviors through four practical criteria derived from diverse disciplines. |
| Outcome: | The proposed framework defines three complexity levels and encompasses 20 causal graph-based tasks across 20 different levels. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
Causal Inference with Large Language Model: A Survey (2025.findings-naacl)
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| Challenge: | Existing causal inference frameworks do not match human judgment in several key areas, such as domain knowledge, logical inference, and cultural context. |
| Approach: | They propose to apply large language models to causal inference tasks . they summarize the main causal problems and approaches and compare their results . |
| Outcome: | The proposed methods are compared with traditional methods in healthcare, finance, and economics. |
Com2 : A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models (2025.acl-long)
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Kai Xiong, Xiao Ding, Yixin Cao, Yuxiong Yan, Li Du, Yufei Zhang, Jinglong Gao, Jiaqian Liu, Bing Qin, Ting Liu
| Challenge: | Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure. |
| Approach: | They propose to build a benchmark for large language models based on complex commonsense reasoning based upon causal event graphs and causal theory. |
| Outcome: | The proposed benchmark combines a complex commonsense reasoning benchmark with a detective story to achieve a more challenging subset. |
Can Large Language Models Infer Causal Relationships from Real-World Text? (2026.acl-long)
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| Challenge: | Existing work evaluating large language models relies on synthetic or simplified texts with explicit causal relationships. |
| Approach: | They develop a benchmark to evaluate LLMs' ability to infer causal relationships from texts . they use a dataset of texts with different levels of explicitness and complexity . |
| Outcome: | The proposed benchmark is the first-ever real-world dataset for this task. |
LLMs Are Prone to Fallacies in Causal Inference (2024.emnlp-main)
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| Challenge: | Recent work shows that causal facts can be extracted from LLMs through prompting . but it is unclear if this success is limited to explicitly-mentioned causal facts in pretraining data . |
| Approach: | They fine tune LLMs on synthetic data and test whether they can infer causal relations . they find that LLM can correctly deduce absence of causal relations from temporal and spatial relations if order is randomized . |
| Outcome: | The proposed model outperforms existing methods on causal inference tasks. |
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables (2025.findings-emnlp)
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| Challenge: | randomized experiments provide strong inferences, but are often infeasible due to ethical or practical constraints. |
| Approach: | They propose a benchmark where the objective is to complete a partial causal graph. |
| Outcome: | The proposed benchmarks show that they can hypothesize backdoor variables between a cause and its effect. |
Can Post-Training Transform LLMs into Causal Reasoners? (2026.findings-acl)
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| Challenge: | Causal inference is a core component of human cognition and requires decision-makers to distinguish between causation and association. |
| Approach: | They propose a dataset comprising seven core causal tasks for training and five diverse test sets and evaluate five different post-training approaches. |
| Outcome: | The proposed model achieves 93.5% accuracy on the CaLM benchmark, compared to 55.4% by OpenAI o3. |