  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.
