Large Language Models as Co-Pilots for Causal Inference in Medical Studies

TL;DR

This paper investigates how large language models can serve as co-pilots to assist medical researchers in identifying and addressing biases and flaws in causal inference from observational clinical data.

Contribution

It introduces a conceptual framework for using LLMs as causal co-pilots, integrating domain knowledge and natural language interaction to improve study design validity.

Findings

Proposes a structured framework for LLMs in causal inference

Provides illustrative examples of LLMs assisting in study design

Highlights challenges and opportunities in deploying LLMs for epidemiology

Abstract

The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about medical interventions. Many published studies are flawed because they violate these assumptions and entail biases such as residual confounding, selection bias, and misalignment between treatment and measurement times. Although researchers are aware of these pitfalls, they continue to occur because anticipating and addressing them in the context of a specific study can be challenging without a large, often unwieldy, interdisciplinary team with extensive expertise. To address this expertise gap, we explore the use of large language models (LLMs) as co-pilot tools to assist researchers in identifying study design flaws that undermine the validity of causal inferences. We propose a conceptual framework for LLMs as causal co-pilots that encode domain knowledge across various fields, engaging with researchers in natural language interactions to provide contextualized assistance in study design. We provide illustrative examples of how LLMs can function as causal co-pilots, propose a structured framework for their grounding in existing causal inference frameworks, and highlight the unique challenges and opportunities in adapting LLMs for reliable use in epidemiological research.