A Review of Dataset and Labeling Methods for Causality Extraction (2020.coling-main)
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| Challenge: | Existing methods for causal relationship extraction are limited and lack of unified methods hinder progress in the field. |
| Approach: | They propose to summarize existing methods and propose a new causal sequence label method . they propose to use multiple candidate causal label sequences according to label controversy . |
| Outcome: | The proposed method summarises existing methods and explores their practicability and extensibility from multiple perspectives. |
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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. |
| Approach: | They propose to use text classifiers to conduct causal analyses on simulated and Yelp data. |
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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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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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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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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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Event Causality Is Key to Computational Story Understanding (2024.naacl-long)
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| Challenge: | Cognitive science and symbolic AI research suggest that event causality provides vital information for story understanding. |
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Causal Inference of Script Knowledge (2020.emnlp-main)
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| Challenge: | Prior work on script induction relied on correlation between instances of events in corpus . instead, we propose an approach based on causal effects between events . |
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Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)
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| Challenge: | a study aims to identify all the event causal relations in a document, both within a sentence and across sentences . main challenges for achieving comprehensive causal relation identification are sparse among all possible event pairs . few causal relations are explicitly stated, especially for identifying cross-sentence causal relations . |
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A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
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Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens (2022.emnlp-main)
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| Challenge: | 'spurious correlations' have been used in NLP to informally denote any undesirable feature-label correlations. |
| Approach: | They formalize this distinction using a causal model and probabilities of necessity and sufficiency, which delineates causal relations between a feature and a label. |
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