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<p>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.</p>

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Methodological and regulatory considerations for causal AI in drug development

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Hana Lee,
  • Sky Qiu,
  • Spencer Haupert,
  • Gabriel K. Innes,
  • Tristan Naumann,
  • Demissie Alemayehu,
  • Mark van der Laan

摘要

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.