mlsanityData to 7 Oct 2026

ROCK: Causal Inference Principles for Reasoning about Commonsense Causality

arXiv 2202.00436 ↗First posted 31 Jan 2022cs.CL cs.AI cs.LGcited by 16

On the map: LLM reasoning → Causal reasoning in LLMs

Abstract (arXiv)

Commonsense causality reasoning (CCR) aims at identifying plausible causes and effects in natural language descriptions that are deemed reasonable by an average person. Although being of great academic and practical interest, this problem is still shadowed by the lack of a well-posed theoretical framework; existing work usually relies on deep language models wholeheartedly, and is potentially susceptible to confounding co-occurrences. Motivated by classical causal principles, we articulate the central question of CCR and draw parallels between human subjects in observational studies and natural languages to adopt CCR to the potential-outcomes framework, which is the first such attempt for commonsense tasks. We propose a novel framework, ROCK, to Reason O(A)bout Commonsense K(C)ausality, which utilizes temporal signals as incidental supervision, and balances confounding effects using temporal propensities that are analogous to propensity scores. The ROCK implementation is modular and zero-shot, and demonstrates good CCR capabilities.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. Builds on 21 of its 65 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

21 of 65 references.

References · 21

  1. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  2. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  3. 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
  4. 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
  5. 2021Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Modelscited by 219
  6. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 177
  7. 2020Unsupervised Commonsense Question Answering with Self-Talkcited by 161
  8. 2020GLUCOSE: GeneraLized and COntextualized Story Explanationscited by 85
  9. 2018Event2Mind: Commonsense Inference on Events, Intents, and Reactionscited by 82
  10. 2019Joint Reasoning for Temporal and Causal Relationscited by 60
  11. 2020Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimatescited by 59
  12. 2019When Choosing Plausible Alternatives, Clever Hans can be Clevercited by 39
9 more
  1. 2020Temporal Common Sense Acquisition with Minimal Supervisioncited by 38
  2. 2020Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoningcited by 33
  3. 2019An Improved Neural Baseline for Temporal Relation Extractioncited by 32
  4. 2019A Structured Learning Approach to Temporal Relation Extractioncited by 24
  5. 2018Improving Temporal Relation Extraction with a Globally Acquired Statistical Resourcecited by 17
  6. 2020Incidental Supervision: Moving beyond Supervised Learningcited by 13
  7. 2021Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentationcited by 9
  8. 2021Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Modelscited by 5
  9. 2017Detecting and Explaining Causes From Text For a Time Series Eventcited by 3

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 6; 2024: 5; 2025: 1.

16 papers.

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  1. 2023Large Language Models Are Not Strong Abstract Reasonerscited by 52
  2. 2024Large Language Models and Causal Inference in Collaboration: A Surveycited by 26
  3. 2022Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Modelscited by 24
  4. 2023COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspectivecited by 22
  5. 2023Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraintscited by 21
  6. 2024A Causal Explainable Guardrails for Large Language Modelscited by 18
  7. 2023Event Causality Is Key to Computational Story Understandingcited by 11
  8. 2023An Overview Of Temporal Commonsense Reasoning and Acquisitioncited by 10
  9. 2024Counterfactual Causal Inference in Natural Language with Large Language Modelscited by 7
  10. 2024The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoningcited by 5
  11. 2024CASA: Causality-driven Argument Sufficiency Assessmentcited by 4
  12. 2023TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Miningcited by 3
4 more
  1. 2022Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Textscited by 2
  2. 2022NECE: Narrative Event Chain Extraction Toolkitcited by 2
  3. 2025Event Causality Identification with Synthetic Controlcited by 2
  4. 2022Are All Steps Equally Important? Benchmarking Essentiality Detection of Eventscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023CLadder: Assessing Causal Reasoning in Language Models
  2. 2025Com2: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models
  3. 2024A Critical Review of Causal Reasoning Benchmarks for Large Language Models
  4. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
  5. 2025Compositional Causal Reasoning Evaluation in Language Models
  6. 2024COLD: Causal reasOning in cLosed Daily activities
  7. 2025HCR-Reasoner: Synergizing Large Language Models and Theory for Human-like Causal Reasoning
  8. 2026NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise