# COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective

arXiv: https://arxiv.org/abs/2305.05191 · HTML: https://mlsanity.com/paper/2305.05191/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning.
- Builds on 45 of its 84 references that are in the map.
- First posted on arXiv 9 May 2023.

## Placed in

- LLM reasoning / [Causal reasoning in LLMs](https://mlsanity.com/lr/causal-reasoning-in-llms/)

## Builds on (most cited first, 30 of 45)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://mlsanity.com/paper/2101.00288/) (2021)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Unsupervised Commonsense Question Answering with Self-Talk](https://mlsanity.com/paper/2004.05483/) (2020)
- [ASER: A Large-scale Eventuality Knowledge Graph](https://mlsanity.com/paper/1905.00270/) (2019)
- [Counterfactual Story Reasoning and Generation](https://mlsanity.com/paper/1909.04076/) (2019)
- [GLUCOSE: GeneraLized and COntextualized Story Explanations](https://mlsanity.com/paper/2009.07758/) (2020)
- [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://mlsanity.com/paper/1805.06939/) (2018)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [Zero-Shot Transfer Learning for Event Extraction](https://mlsanity.com/paper/1707.01066/) (2017)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://mlsanity.com/paper/2005.00649/) (2020)
- [Temporal Reasoning on Implicit Events from Distant Supervision](https://mlsanity.com/paper/2010.12753/) (2020)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models](https://mlsanity.com/paper/2005.10389/) (2020)
- [When Choosing Plausible Alternatives, Clever Hans can be Clever](https://mlsanity.com/paper/1911.00225/) (2019)
- [Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning](https://mlsanity.com/paper/2004.14074/) (2020)
- [Automatic Story Generation: Challenges and Attempts](https://mlsanity.com/paper/2102.12634/) (2021)
- [ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities](https://mlsanity.com/paper/2104.02137/) (2021)
- [Analogous Process Structure Induction for Sub-event Sequence Prediction](https://mlsanity.com/paper/2010.08525/) (2020)

## Built on by (most cited first, 22 of 22)

- [Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation](https://mlsanity.com/paper/2305.07375/) (2023)
- [SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval](https://mlsanity.com/paper/2401.13478/) (2024)
- [CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting](https://mlsanity.com/paper/2409.19058/) (2024)
- [AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation](https://mlsanity.com/paper/2402.10646/) (2024)
- [Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning](https://mlsanity.com/paper/2305.14970/) (2023)
- [CausalEval: Towards Better Causal Reasoning in Language Models](https://mlsanity.com/paper/2410.16676/) (2024)
- [Failure Modes of LLMs for Causal Reasoning on Narratives](https://mlsanity.com/paper/2410.23884/) (2024)
- [Event Causality Is Key to Computational Story Understanding](https://mlsanity.com/paper/2311.09648/) (2023)
- [ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty](https://mlsanity.com/paper/2412.20251/) (2024)
- [CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention](https://mlsanity.com/paper/2506.00519/) (2025)
- [LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model](https://mlsanity.com/paper/2312.17122/) (2023)
- [CodeGraph: Enhancing Graph Reasoning of LLMs with Code](https://mlsanity.com/paper/2408.13863/) (2024)
- [A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects](https://mlsanity.com/paper/2411.10371/) (2024)
- [The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning](https://mlsanity.com/paper/2406.19307/) (2024)
- [QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering](https://mlsanity.com/paper/2310.11303/) (2023)
- [TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining](https://mlsanity.com/paper/2310.05210/) (2023)
- [Choice-75: A Dataset on Decision Branching in Script Learning](https://mlsanity.com/paper/2309.11737/) (2023)
- [Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering](https://mlsanity.com/paper/2403.11129/) (2024)
- [KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining](https://mlsanity.com/paper/2407.19740/) (2024)
- [Event Causality Identification with Synthetic Control](https://mlsanity.com/paper/2509.18156/) (2025)
- [Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?](https://mlsanity.com/paper/2402.14355/) (2024)
- [Causal Retrieval with Semantic Consideration](https://mlsanity.com/paper/2504.04700/) (2025)

## Abstract (arXiv)

Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile, previous works about commonsense causation only consider two events and ignore their context, simplifying the task formulation. This paper proposes a new task to detect commonsense causation between two events in an event sequence (i.e., context), called contextualized commonsense causal reasoning. We also design a zero-shot framework: COLA (Contextualized Commonsense Causality Reasoner) to solve the task from the causal inference perspective. This framework obtains rich incidental supervision from temporality and balances covariates from multiple timestamps to remove confounding effects. Our extensive experiments show that COLA can detect commonsense causality more accurately than baselines.