VIGOR+: LLM-Driven Confounder Generation and Validation

The paper likely introduces a novel method for identifying and validating confounders in causal inference using a Large Language Model (LLM) within a feedback loop. The iterative approach, likely involving a CEVAE (Conditional Ensemble Variational Autoencoder), suggests an attempt to improve robustness and accuracy in identifying confounding variables.

• •Proposes a novel method for confounder identification.
• •Utilizes a Large Language Model (LLM) and CEVAE.
• •Employs an iterative feedback loop for validation.