Cause and Effect: Can Large Language Models Truly Understand Causality?

TL;DR

This work tackles whether large language models truly understand causality by introducing CARE-CA, a hybrid architecture that fuses explicit external knowledge from ConceptNet with implicit LLM reasoning and counterfactual explanations. It combines Context Knowledge Integrator, Counterfactual Reasoning Enhancer, and Context-Aware Prompting Mechanism to bolster causal relationship identification, causal discovery, and counterfactual reasoning, while introducing CausalNet as a dedicated benchmark. Empirical results show CARE-CA achieving state-of-the-art performance across multiple tasks and datasets, notably 94.6% accuracy on CausalNet and strong gains on COPA, Cladder, Com2sense, and e-care. The findings support the value of integrating structured knowledge with counterfactual reasoning to improve interpretability and reliability of AI systems in causal inference tasks.

Abstract

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.

Figures (4)

• Figure 1: Causal reasoning without CARE-CA: Given the premise "My body cast a shadow over the grass.", the left hypothesis, "The sun was rising," should be identified as the cause to arrive at the correct hypothesis conclusion.
• Figure 2: Causal Reasoning Enhanced with CARE-CA: Starting from a premise, causal hypotheses are evaluated. Integration of external knowledge from ConceptNet enhances understanding. Contextual prompting adapts hypotheses to the time of day. Counterfactual reasoning explores alternative scenarios. Improved causal reasoning is achieved by incorporating context and counterfactuals, leading to the identification of the correct hypothesis.
• Figure 3: Enhancing LLM Causal Understanding via Structured Knowledge and Counterfactuals: This approach integrates ConceptNet knowledge graphs and 'what-if' scenarios to improve LLMs' causal reasoning, using CKI, CRE, and CAPM to boost performance on causal benchmarks like CARE-CA.
• Figure 4: Performance comparison of causal reasoning models across datasets, highlighting the effectiveness of the CausalNet dataset and the CARE-CA model.