| Challenge: | Understanding causal explanations is an important psychological factor linked to physical and mental health. |
| Approach: | They propose to automate causal explanation analysis by building on discourse parsing and using a hierarchy of Bidirectional LSTMs to identify the specific phrase that is the explanation. |
| Outcome: | The proposed subtasks achieve strong accuracies but differ in their approaches . the proposed sub task is compared with the previous task and is able to identify the specific phrase that is the explanation. |
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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
| Challenge: | causality has not had the same importance in natural language processing, says aaron e. smith . he says research on causality in NLP remains scattered across domains without unified definitions . |
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| Outcome: | The proposed method is a unified overview of causal inference for the NLP community. |
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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CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts (2022.lrec-1)
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| Challenge: | Social media platforms are important resources for investigating mental health of users. |
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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. |
| Approach: | They propose a method for event causality identification that leads to material improvements in story understanding. |
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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 . |
| 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. |
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A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse (2025.emnlp-main)
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| Challenge: | Existing datasets focus on explicit causality in structured text, providing limited support for detecting implicit causal expressions. |
| Approach: | They propose a dataset of Reddit posts annotated across four causal tasks . they use a binary causal classification, explicit vs. implicit causality, cause–effect span extraction and causal gist generation to bridge causal detection and reasoning over informal discourse. |
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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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Mapping Long-term Causalities in Psychiatric Symptomatology and Life Events from Social Media (2024.naacl-long)
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Siyuan Chen, Meilin Wang, Minghao Lv, Zhiling Zhang, Juqianqian Juqianqian, Dejiyangla Dejiyangla, Yujia Peng, Kenny Zhu, Mengyue Wu
| Challenge: | Existing studies focus on the semantic content of social media posts, overlooking the evolving nature of mental disorders and symptoms. |
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e-CARE: a New Dataset for Exploring Explainable Causal Reasoning (2022.acl-long)
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| Challenge: | Existing causal reasoning models only learn to induce empirical causal patterns that are predictive to the label, while human beings seek for deep and conceptual understanding of the causality to explain the observed causal facts. |
| Approach: | They present a human-annotated CAusal REasoning dataset with conceptual explanations of the causality. |
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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 . |
| Approach: | They propose to identify all event causal relations in a document, both within a sentence and across sentences. |
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