# Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey

arXiv: https://arxiv.org/abs/1605.07895 · HTML: https://mlsanity.com/paper/1605.07895/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

## Built on by (most cited first, 16 of 16)

- [Guided Generation of Cause and Effect](https://mlsanity.com/paper/2107.09846/) (2021)
- [A Survey on Extraction of Causal Relations from Natural Language Text](https://mlsanity.com/paper/2101.06426/) (2021)
- [The Causal News Corpus: Annotating Causal Relations in Event Sentences from News](https://mlsanity.com/paper/2204.11714/) (2022)
- [UniCausal: Unified Benchmark and Repository for Causal Text Mining](https://mlsanity.com/paper/2208.09163/) (2022)
- [A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects](https://mlsanity.com/paper/2411.10371/) (2024)
- [HeadlineCause: A Dataset of News Headlines for Detecting Causalities](https://mlsanity.com/paper/2108.12626/) (2021)
- [The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning](https://mlsanity.com/paper/2406.19307/) (2024)
- [A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms](https://mlsanity.com/paper/2209.06367/) (2022)
- [Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022](https://mlsanity.com/paper/2211.12154/) (2022)
- [CURIE: An Iterative Querying Approach for Reasoning About Situations](https://mlsanity.com/paper/2104.00814/) (2021)
- [Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work](https://mlsanity.com/paper/2108.03087/) (2021)
- [Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks](https://mlsanity.com/paper/2107.09980/) (2021)
- [Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion](https://mlsanity.com/paper/2102.09923/) (2021)
- [Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities](https://mlsanity.com/paper/2409.04934/) (2024)
- [Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution](https://mlsanity.com/paper/2512.11108/) (2025)
- [iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations](https://mlsanity.com/paper/2604.06902/) (2026)

## Abstract (arXiv)

Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on small and domain-specific data sets. However, with the advent of big data, the availability of affordable computing power and the recent popularization of machine learning, the paradigm to tackle this problem has slowly shifted. Machines are now expected to learn generic causal extraction rules from labelled data with minimal supervision, in a domain independent-manner. In this paper, we provide a comprehensive survey of causal relation extraction techniques from both paradigms, and analyse their relative strengths and weaknesses, with recommendations for future work.