| 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. |
| Outcome: | The proposed method outperforms state-of-the-art methods and can underpin auditing of language models for (un)desirable causal effects, such as misinformation or discrimination. |
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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 . |
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CodeSCM: Causal Analysis for Multi-Modal Code Generation (2025.naacl-long)
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| Challenge: | Prior work has shown that multimodal prompts can be highly sensitive, where small adjustments might result in drastically different responses from the model. |
| Approach: | They propose a Structural Causal Model (SCM) for analyzing multi-modal code generation using large language models (LLMs). |
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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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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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Challenges of Using Text Classifiers for Causal Inference (D18-1)
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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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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. |
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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. |
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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. |
| Approach: | They propose a pipeline that leverages a Large Language Model to identify causal claims made in natural language documents and applies a clustering algorithm to group causal claims based on their semantic topics. |
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Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2022.tacl-1)
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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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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. |
| Approach: | They propose to use LLM-based nuisance models to estimate causal effects from non-randomized data using assumptions about the underlying data distribution. |
| Outcome: | The proposed method reduces the relative absolute error by 10.4% over existing methods on the best available dataset. |