mlsanityData to 7 Oct 2026

ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning

arXiv 2502.08148 ↗First posted 12 Feb 2025cs.AIcited by 3

On the map: LLM reasoning → Causal reasoning in LLMs

Abstract (arXiv)

Identifying cause-and-effect relationships is critical to understanding real-world dynamics and ultimately causal reasoning. Existing methods for identifying event causality in NLP, including those based on Large Language Models (LLMs), exhibit difficulties in out-of-distribution settings due to the limited scale and heavy reliance on lexical cues within available benchmarks. Modern benchmarks, inspired by probabilistic causal inference, have attempted to construct causal graphs of events as a robust representation of causal knowledge, where textttCRAB citepromanou2023crab is one such recent benchmark along this line. In this paper, we introduce textttACCESS, a benchmark designed for discovery and reasoning over abstract causal events. Unlike existing resources, textttACCESS focuses on causality of everyday life events on the abstraction level. We propose a pipeline for identifying abstractions for event generalizations from textttGLUCOSE citepmostafazadeh-etal-2020-glucose, a large-scale dataset of implicit commonsense causal knowledge, from which we subsequently extract 1,4K causal pairs. Our experiments highlight the ongoing challenges of using statistical methods and/or LLMs for automatic abstraction identification and causal discovery in NLP. Nonetheless, we demonstrate that the abstract causal knowledge provided in textttACCESS can be leveraged for enhancing QA reasoning performance in LLMs.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. Builds on 26 of its 87 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

26 of 87 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation12 May
  2. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causality28 Apr

Other references · 24

  1. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
  4. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
  5. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  6. 2020COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphscited by 254
  7. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  8. 2019Learning Sparse Nonparametric DAGscited by 207
  9. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 186
  10. 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 146
  11. 2023Can Large Language Models Infer Causation from Correlation?cited by 142
  12. 2023Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarkscited by 139
12 more
  1. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
  2. 2020GLUCOSE: GeneraLized and COntextualized Story Explanationscited by 85
  3. 2022MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extractioncited by 65
  4. 2021gCastle: A Python Toolbox for Causal Discoverycited by 60
  5. 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
  6. 2021Ordering-Based Causal Discovery with Reinforcement Learningcited by 55
  7. 2023CRAB: Assessing the Strength of Causal Relationships Between Real-world Eventscited by 27
  8. 2021Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statementcited by 21
  9. 2021Improving Paraphrase Detection with the Adversarial Paraphrasing Taskcited by 19
  10. 2022Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualizationcited by 17
  11. 2023Event Causality Is Key to Computational Story Understandingcited by 11
  12. 2023An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graphcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026ClimateCause: Complex and Implicit Causal Structures in Climate Reportscited by 1
  2. 2026Implicit Causal Graph Construction in Text via Chain Discoverycited by 0
  3. 2026Abstract Event Causal Rules: Induction and Applicationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Causal Graph based Event Reasoning using Semantic Relation Experts
  2. 2024A Critical Review of Causal Reasoning Benchmarks for Large Language Models
  3. 2022Towards Fine-grained Causal Reasoning and QA
  4. 2024CausalGraph2LLM: Evaluating LLMs for Causal Queries
  5. 2026Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
  6. 2025Causal Reasoning in Pieces: Modular In-Context Learning for Causal Discovery
  7. 2024Large Language Models for Constrained-Based Causal Discovery
  8. 2026CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching