This survey explores how Large Language Models are transforming traditional causal inference approaches, combining mathematical reasoning with natural language capabilities.

• Integrates human knowledge, reasoning, and data mining for enhanced causal analysis
• Presents comprehensive review of LLM applications in causal inference tasks
• Examines specific applications across medical research and healthcare contexts
• Highlights new opportunities for solving complex causal problems

Why it matters for medicine: By leveraging LLMs for causal inference, medical researchers can potentially identify treatment effects, understand disease mechanisms, and develop more personalized interventions with greater efficiency and accuracy than traditional methods alone.

Causal Inference with Large Language Model: A Survey