| Challenge: | a number of scientific analyses focus on low-dimensional structured data, but text classifiers can be used to produce structured variables. |
| Approach: | They propose to use text classifiers to conduct causal analyses on simulated and Yelp data. |
| Outcome: | The proposed method can be used on simulated and Yelp data. |
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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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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. |
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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 . |
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| Challenge: | Existing methods for causal relationship extraction are limited and lack of unified methods hinder progress in the field. |
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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. |
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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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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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DoubleLingo: Causal Estimation with Large Language Models (2024.naacl-short)
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| Challenge: | Existing methods for causal estimation are inadequate for noisy text data. |
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CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)
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Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
| Challenge: | Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world. |
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Language Models as Causal Effect Generators (2025.emnlp-main)
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| Challenge: | Using sequence-driven structural causal models (SD-SCMs) we characterize how SD-SCAMs enables sampling from observational, interventional, and counterfactual distributions according to the desired causal structure. |
| Approach: | They propose a sequence-driven structural causal model that uses language models to parameterize a structural causal system based on a user-specified DAG. |
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