Papers with causal discovery
CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing (2022.emnlp-tutorials)
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| Challenge: | Establishing causal relationships is a fundamental goal of scientific research . lack of clear definitions, notations, benchmark datasets, and challenges remains . |
| Approach: | They introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provide an overview of causal perspectives to NLP problems. |
| Outcome: | This tutorial introduces the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provides an overview of causal perspectives to NLP problems. |
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery (2025.findings-acl)
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| Challenge: | Existing statistical causal discovery methods rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships. |
| Approach: | They propose a multi-agent system powered by tool-augmented Large Language Models that can combine data from multiple modalities and integrate multi-modal data for knowledge-driven reasoning. |
| Outcome: | The proposed system has two agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven reasoning. |
ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning (2025.naacl-long)
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Vy Vo, Lizhen Qu, Tao Feng, Yuncheng Hua, Xiaoxi Kang, Songhai Fan, Tim Dwyer, Lay-Ki Soon, Gholamreza Haffari
| Challenge: | Existing methods for identifying event causality in NLP are limited in their scale and rely on lexical cues. |
| Approach: | They propose a benchmark for identifying abstract causality from a large-scale dataset. |
| Outcome: | The proposed benchmark can be leveraged for enhancing QA reasoning performance in LLMs. |
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang (2022.acl-long)
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| Challenge: | a recent study suggests that language evolution is a diachronic process, but no causal analysis is performed to verify these claims. |
| Approach: | They analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang terms. |
| Outcome: | The proposed model shows that slang has smaller semantic change but larger frequency shifts over time. |
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why? (2023.emnlp-main)
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| Challenge: | a systematic framework to analyze the evolution of research topics in a scientific field is crucial for keeping abreast of its continuous advancement. |
| Approach: | They propose a framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. |
| Outcome: | The proposed framework uncovers evolutionary trends and causes for a wide range of NLP topics. |
iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations (2026.acl-long)
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| Challenge: | Lack of causally annotated text data for use as ground truth hinders causal discovery . early template-based generation methods sacrifice text naturalness in exchange for high annotation costs . |
| Approach: | They propose a method which performs real-world concept assignment to nodes before converting causal graphs into text. |
| Outcome: | The proposed method shows high annotation accuracy and naturalness across extensive tests. |
IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data (2025.acl-long)
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| Challenge: | Existing statistical methods for causal discovery are expensive, require high-quality structured tabular data, and are often not available for a wide range of NLP applications. |
| Approach: | They propose a framework that combines statistical and large language model methods to discover causal relations from a set of initial variables. |
| Outcome: | The proposed method combines statistical and LLM-based methods to discover known and novel causal relations. |
On the Reliability of Large Language Models for Causal Discovery (2025.acl-long)
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| Challenge: | Existing statistical methods to identify causal relationships from observational data remain elusive. |
| Approach: | They examine the impact of memorization for accurate causal relation prediction, the influence of incorrect causal relations in pre-training data and the contextual nuances that influence LLMs’ understanding of causal relations. |
| Outcome: | The proposed models are effective in recognizing causal relations that occur frequently in pre-training data, but their ability to generalize to new or rare causal relations is limited. |
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction (2024.findings-emnlp)
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Yuncheng Hua, Yujin Huang, Shuo Huang, Tao Feng, Lizhen Qu, Christopher Bain, Richard Bassed, Reza Haf
| Challenge: | Emotion-cause pair extraction is a task that aims to extract emotions and the events causing such emotions. |
| Approach: | They propose a deep latent model which captures the underlying latent structures of data and utilizes the easily transferable knowledge of emotions as the bridge to link the distributions of events in different domains. |
| Outcome: | The proposed model outperforms the strongest baseline by approximately 11.05% on a Chinese benchmark and 2.45% on an English benchmark in terms of weighted-average F1 score. |
CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models (2025.emnlp-main)
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| Challenge: | Large vision-language models have shown impressive ability in various language tasks, especially with their emergent in-context learning capability. |
| Approach: | They propose a causal reasoning benchmark for multi-modal in-context learning from large vision-language models that incorporates visual inputs. |
| Outcome: | The proposed model outperforms existing models on three visual causal reasoning tasks and demonstrates their strengths and weaknesses. |
Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series? (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) and Multimodal LLMs (MLLMs) show strong performance in complex reasoning tasks, but their ability to extract symbolic laws from time series data remains underexplored. |
| Approach: | They propose a benchmark to assess symbolic reasoning over real-world time series across three tasks: multivariate symbolic regression, Boolean network inference, and causal discovery. |
| Outcome: | The proposed framework integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system. |