LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
用于因果推断中自动混杂发现与子群分析的大语言模型智能体
Estimating individualized treatment effects from observational data presents a persistent challenge due to unmeasured confounding and structural bias. Causal Machine Learning (causal ML) methods, such as causal trees and doubly robust estimators, provide tools for estimating conditional average treatment effects. These methods have limited effectiveness in complex real-world environments due to the presence of latent confounders or those described in unstructured formats.…