Marika NiihoriWe are delighted to share the publication of “Causal inference and digital twins: a roadmap for the future of clinical trials” in npj Digital Medicine.
The new paper is the result of a major collaboration between researchers from the van der Schaar Lab and senior leaders across pharmaceutical R&D, clinical research, data science and consulting.
Clinical trials are essential for determining whether treatments are safe and effective, but they remain slow, expensive and operationally complex. The paper sets out how causal inference could help researchers understand which treatments work, for whom and under what circumstances, while digital twins could enable them to explore potential outcomes under alternative treatments or trial designs. Together, these approaches could support more adaptive, efficient and patient-centred clinical development.
The publication builds on the 2025 manifesto “Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation,” which brought together perspectives from academia and industry around a shared vision for AI-enabled clinical development. The new peer-reviewed Perspective develops this vision into a roadmap, outlining the opportunities, technical requirements and practical challenges involved in translating causal inference and digital twins into clinical trials.
The breadth of collaboration behind the paper is central to its significance.
As Tony Wood, Chief Scientific Officer, Head of R&D and a member of the Executive Committee at GSK, explains:
“At GSK, we are advancing a more dynamic model of clinical development in which data, technology, and collaboration are tightly integrated. AI methodologies such as causal inference and digital twins enable a more rigorous characterisation of treatment effects, improved identification of responsive patient subgroups, and more efficient trial designs. By integrating these approaches, we can enable more adaptive, precise, and patient-centred development and accelerate the delivery of truly transformative medicines.”
Ramon Hernandez Vecino, Global Head of Development Real World Evidence at Sanofi, highlights the importance of translating methodological progress into practice:
“This roadmap comes at a pivotal moment as the industry moves from exploring AI concepts to implementing them in drug development. While many organizations are discussing the potential of causal inference and digital twins, Sanofi is uniquely positioned to help operationalize these approaches at scale, translating methodological innovation into practical solutions that support clinical development and evidence generation. We hope this work will help accelerate the responsible adoption of AI-driven approaches across the industry.”
For Justine Rochon, Head of R&D Data & Quantitative Sciences at Takeda, the collaboration itself is fundamental to the importance of the work:
“This work is close to my heart because it shows the power of genuine multidisciplinary, multistakeholder collaboration. By working together, we can see what others have not yet seen, take ideas further, and turn them into real progress for patients.”
The paper provides a roadmap for the rigorous and responsible development of these methods, including the need for representative data, transparent assumptions, robust validation and close engagement with clinical, statistical and regulatory expertise.
Read the full paper: Causal inference and digital twins: a roadmap for the future of clinical trials.
Mihaela van der Schaar
