Facilitating the Adoption of Causal Infer-ence Methods Through LLM-Empowered Co-Pilot

The research introduces CATE-B, an **open-source co-pilot system** designed to **simplify causal inference** for non-experts. This system **leverages large language models (LLMs)** to guide users through the complex process of estimating treatment effects from observational data. CATE-B assists in **constructing structural causal models**, **identifying robust adjustment sets** using a novel "Minimal Uncertainty Adjustment Set" criterion, and **selecting appropriate regression methods**. By integrating LLMs and causal discovery algorithms, CATE-B aims to **lower the barrier to rigorous causal analysis** and promote the widespread adoption of advanced causal inference techniques. The authors also provide a **benchmark suite** to encourage reproducibility and evaluation of LLM-augmented causal inference pipelines.