| Challenge: | Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. |
| Approach: | They developed a prediction model that classifies conclusion sentences into “no relationship”, “correlational”, “conditional causal” and “direct causal” categories. |
| Outcome: | The proposed model can be used to identify the inappropriate use of causal language in scientific publications and news articles. |
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Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
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| Challenge: | a number of scientific analyses focus on low-dimensional structured data, but text classifiers can be used to produce structured variables. |
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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. |
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| Challenge: | Existing language models lack a conceptual framework for understanding causal graphs, but there is still potential for improvement. |
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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
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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 . |
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Pipeline for modeling causal beliefs from natural language (2023.acl-demo)
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| Challenge: | Existing methods to analyze language data for psychological causality are difficult to advance as they do not isolate cognitive mechanisms. |
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Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020.acl-main)
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| Challenge: | Unmeasured or latent confounders can bias causal estimates and this has motivated interest in measuring potential confounder from observed text. |
| Approach: | They propose to use text to measure potential confounders in a way that allows for a rich measurement of multiple confounder variables. |
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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. |
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