With the rise of Large Language Models(LLMs), it has become crucial to
understand their capabilities and limitations in deciphering and explaining the
complex web of causal relationships that language entails. Current methods use
either explicit or implicit causal reasoning, yet there is a strong need for a
unified approach combining both to tackle a wide array of causal relationships
more effectively. This research proposes a novel architecture called Context
Aware Reasoning Enhancement with Counterfactual Analysis(CARE CA) framework to
enhance causal reasoning and explainability. The proposed framework
incorporates an explicit causal detection module with ConceptNet and
counterfactual statements, as well as implicit causal detection through LLMs.
Our framework goes one step further with a layer of counterfactual explanations
to accentuate LLMs understanding of causality. The knowledge from ConceptNet
enhances the performance of multiple causal reasoning tasks such as causal
discovery, causal identification and counterfactual reasoning. The
counterfactual sentences add explicit knowledge of the not caused by scenarios.
By combining these powerful modules, our model aims to provide a deeper
understanding of causal relationships, enabling enhanced interpretability.
Evaluation of benchmark datasets shows improved performance across all metrics,
such as accuracy, precision, recall, and F1 scores. We also introduce
CausalNet, a new dataset accompanied by our code, to facilitate further
research in this domain.

提出了一种新颖的架构称为 “具有反事实分析的上下文感知推理增强框架”，通过将显式和隐式因果推理相结合，利用 ConceptNet 和反事实语句来提高因果推理和可解释性，进一步提供对因果关系的深度理解和促进可解释性。