Confounder Hunters: How LLM Agents are Rewriting the Rules of Causal Inference
TL;DR for operators Clinical analytics teams already know the unpleasant truth: observational data is cheap, rich, and biased in ways that do not politely announce themselves. The paper behind this article proposes a way to make that bias-hunting process less artisanal. Instead of asking experts to manually inspect every causal-tree rule, the framework lets causal trees segment patients, asks medical LLM agents to suggest plausible confounders using decomposed prompting plus retrieval, sends those suggestions through expert validation, then recursively focuses on samples whose treatment-effect estimates still have wide confidence intervals.1 ...