LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding

A recent study discusses the challenges of estimating treatment effects in personalized medicine, particularly when relying on textual descriptions during inference. While models are trained on structured datasets, the shift to unstructured text can complicate predictions. This research is significant as it highlights the need for improved methodologies to ensure accurate treatment estimations, ultimately impacting patient care.