The survey on Causal Inference and Large Language Models (LLMs) examines how causal reasoning improves model performance and contributes to the NLP domain. It also recognizes how LLMs can aid causal inference.

• Addresses enhancing predictive accuracy with causal relationships.
• Focuses on fairness, robustness, and explainability in NLP models.
• Discusses aiding discovery of causal relations and effect estimation with LLMs.
• Covers areas like understanding LLM reasoning and multimodal scenarios.

This evidence-based review elucidates the critical role both causal inference and LLM reasoning play in advancing AI’s capacity to simulate human-like understanding and decision-making. Revelations from this survey could inspire AI developments that are more transparent, ethical, and grounded in reality, conducive to sectors from healthcare to judiciary where decision-making is complex and consequential.