LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference

2025-08-10

AI Summary

Individualized treatment effect estimation is frequently compromised by unmeasured confounding and structural bias; existing causal machine learning methods struggle with identifying unstructured confounders and remain heavily reliant on domain expertise. To address these limitations, we propose the first framework that systematically integrates large language model (LLM) agents into the causal inference pipeline—enabling automated confounder structure discovery and semantic-driven subgroup identification. Our method synergistically couples causal trees, doubly robust estimators, and LLM agents, leveraging their reasoning and natural language understanding capabilities to extract latent confounders from unstructured text data. This reduces manual annotation burden while enhancing interpretability and scalability. Evaluated on real-world clinical datasets, our approach significantly narrows confidence intervals for treatment effects, uncovers previously neglected confounding biases, and improves robustness against unobserved confounding—thereby strengthening the credibility and reliability of causal estimates.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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. Moreover, reliance on domain experts for confounder identification and rule interpretation introduces high annotation cost and scalability concerns. In this work, we proposed Large Language Model-based agents for automated confounder discovery and subgroup analysis that integrate agents into the causal ML pipeline to simulate domain expertise. Our framework systematically performs subgroup identification and confounding structure discovery by leveraging the reasoning capabilities of LLM-based agents, which reduces human dependency while preserving interpretability. Experiments on real-world medical datasets show that our proposed approach enhances treatment effect estimation robustness by narrowing confidence intervals and uncovering unrecognized confounding biases. Our findings suggest that LLM-based agents offer a promising path toward scalable, trustworthy, and semantically aware causal inference.

Problem

• Estimating treatment effects with unmeasured confounding bias
• Automating confounder discovery to reduce expert dependency
• Improving causal inference robustness in complex real-world data

Innovation

• LLM-based agents automate confounder discovery
• Integrate agents into causal ML pipeline
• Enhance robustness via subgroup analysis