Abstract Advances in AI offer significant opportunities to enhance drug development. While several regulatory agencies have begun issuing guidance on AI adoption, its application to causal inference—a critical piece to understand treatment effects and inform regulatory decisions—remains limited. This paper reviews regulatory activities and examines statistical methodologies for AI-driven causal inference. We discuss key regulatory challenges and illustrate how AI adds value across diverse data sources and studies.
Lee et al. (2026) studied this question.