Papers with Causal inference
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. |
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis (2021.naacl-main)
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| Challenge: | Existing studies focus on analyzing structured data, while mining causal relationship among factors from unstructured data is of great importance. |
| Approach: | They propose a graph-based causal inference framework which builds causal graphs from fact descriptions without much human involvement. |
| Outcome: | The proposed framework can capture nuance from fact descriptions among confusing charges and provide explainable discrimination in few-shot settings. |
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. |
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. |
Bayesian Topic Regression for Causal Inference (2021.emnlp-main)
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| Challenge: | a Bayesian topic regression model uses text and numerical information to model outcome variables. |
| Approach: | They propose a Bayesian Topic Regression model that uses both text and numerical information to model an outcome variable. |
| Outcome: | The proposed model recovers ground truth with lower bias than any benchmark model when text and numerical features are correlated. |
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. |