# e-CARE: a New Dataset for Exploring Explainable Causal Reasoning

arXiv: https://arxiv.org/abs/2205.05849 · HTML: https://mlsanity.com/paper/2205.05849/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning.
- Builds on 23 of its 59 references that are in the map.
- First posted on arXiv 12 May 2022.

## Placed in

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

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Training Classifiers with Natural Language Explanations](https://mlsanity.com/paper/1805.03818/) (2018)
- [GenericsKB: A Knowledge Base of Generic Statements](https://mlsanity.com/paper/2005.00660/) (2020)
- [Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering](https://mlsanity.com/paper/2010.03274/) (2020)
- [Guided Generation of Cause and Effect](https://mlsanity.com/paper/2107.09846/) (2021)
- [Adversarial training for multi-context joint entity and relation extraction](https://mlsanity.com/paper/1808.06876/) (2018)
- [Finding Generalizable Evidence by Learning to Convince Q&A Models](https://mlsanity.com/paper/1909.05863/) (2019)
- [Teaching Machine Comprehension with Compositional Explanations](https://mlsanity.com/paper/2005.00806/) (2020)
- [Robust Multilingual Part-of-Speech Tagging via Adversarial Training](https://mlsanity.com/paper/1711.04903/) (2017)

## Built on by (most cited first, 30 of 47)

- [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets](https://mlsanity.com/paper/2307.10928/) (2023)
- [The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems](https://mlsanity.com/paper/2503.03750/) (2025)
- [Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation](https://mlsanity.com/paper/2305.07375/) (2023)
- [Natural Language Reasoning, A Survey](https://mlsanity.com/paper/2303.14725/) (2023)
- [ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://mlsanity.com/paper/2304.14827/) (2023)
- [IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neurons](https://mlsanity.com/paper/2406.18406/) (2024)
- [Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?](https://mlsanity.com/paper/2506.21215/) (2025)
- [GPT-3.5, GPT-4, or BARD? Evaluating LLMs Reasoning Ability in Zero-Shot Setting and Performance Boosting Through Prompts](https://mlsanity.com/paper/2305.12477/) (2023)
- [Causal Inference with Large Language Model: A Survey](https://mlsanity.com/paper/2409.09822/) (2024)
- [Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks](https://mlsanity.com/paper/2504.18838/) (2025)
- [Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts](https://mlsanity.com/paper/2309.17415/) (2023)
- [Cause and Effect: Can Large Language Models Truly Understand Causality?](https://mlsanity.com/paper/2402.18139/) (2024)
- [Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking](https://mlsanity.com/paper/2310.12342/) (2023)
- [Quantifying the Capabilities of LLMs across Scale and Precision](https://mlsanity.com/paper/2405.03146/) (2024)
- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance](https://mlsanity.com/paper/2508.19764/) (2025)
- [CausalEval: Towards Better Causal Reasoning in Language Models](https://mlsanity.com/paper/2410.16676/) (2024)
- [Mitigating Hallucinations in Large Language Models via Causal Reasoning](https://mlsanity.com/paper/2508.12495/) (2025)
- [Through the Lens of Core Competency: Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2308.07902/) (2023)
- [Emerging Synergies in Causality and Deep Generative Models: A Survey](https://mlsanity.com/paper/2301.12351/) (2023)
- [Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation](https://mlsanity.com/paper/2308.00085/) (2023)
- [Self-Evolving GPT: A Lifelong Autonomous Experiential Learner](https://mlsanity.com/paper/2407.08937/) (2024)
- [MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models](https://mlsanity.com/paper/2407.17379/) (2024)
- [Always Tell Me The Odds: Fine-grained Conditional Probability Estimation](https://mlsanity.com/paper/2505.01595/) (2025)
- [Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance](https://mlsanity.com/paper/2403.09085/) (2024)
- [PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood Estimation](https://mlsanity.com/paper/2504.01509/) (2025)
- [Causality extraction from medical text using Large Language Models (LLMs)](https://mlsanity.com/paper/2407.10020/) (2024)
- [A Survey of Methods, Challenges and Perspectives in Causality](https://mlsanity.com/paper/2302.00293/) (2023)
- [EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models](https://mlsanity.com/paper/2305.15268/) (2023)
- [Inference to the Best Explanation in Large Language Models](https://mlsanity.com/paper/2402.10767/) (2024)

## Abstract (arXiv)

Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal facts to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning resources. In this paper, we fill this gap by presenting a human-annotated explainable CAusal REasoning dataset (e-CARE), which contains over 21K causal reasoning questions, together with natural language formed explanations of the causal questions. Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.