Methodological and regulatory considerations for causal AI in drug development

npj Digital Medicine 2026 Open access

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Authors: Hana Lee, Sky Qiu, Spencer R. Haupert, Gabriel K. Innes, Tristan Naumann, Demissie Alemayehu, Mark van der Laan
DOI: 10.1038/s41746-026-02477-w
Cited by: 3
References: 40

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.

Fields of Study & Subjects

Advanced Causal Inference Techniques Artificial Intelligence in Healthcare and Education Explainable Artificial Intelligence (XAI)

Publication details

Journal / source
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Volume
9
Issue
1
Publication date
27 February 2026
Language
EN