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Objective <p>This work proposes a unified framework for causal digital twins, integrating Structural Causal Models (SCMs), the Potential Outcomes Framework, and reinforcement learning. The objective is to enable individualized counterfactual reasoning and dynamic treatment policy optimization, moving beyond prediction toward actionable, ethical, and adaptive decision support.</p> Materials and methods <p>This work formalizes causal digital twins by embedding causal inference principles into digital architectures, modeling both observed and counterfactual outcomes for each patient. Individualized Treatment Effects (ITE) are estimated through causal modeling, and sequential decision-making is optimized using reinforcement learning techniques. This work is a methodological and conceptual research contribution. We propose a unified framework for the construction and evaluation of clinical digital twins and a practical checklist covering validation, safety, and ethical requirements. No new dataset or algorithmic benchmark is introduced.</p> Results <p>This work discusses methodological requirements, learning objectives, evaluation strategies, and ethical considerations necessary for deploying causal digital twins in real-world clinical contexts. The proposed framework allows for accurate estimation of counterfactual outcomes, personalized treatment effect modeling, and dynamic policy learning over time.</p> Discussion <p>Through this integration, digital twins evolve from static predictors into active engines of individualized intervention, providing a new paradigm for precision healthcare. Causal digital twins offer a transformative extension of current digital twin technologies, merging causal reasoning with dynamic simulation to enable personalized, counterfactual-driven clinical decision support.</p> Conclusion <p>This work lays the methodological foundation for operationalizing causal digital twins in future AI-driven healthcare systems, with profound implications for ethical, individualized, and adaptive medicine.</p>

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From prediction to intervention: causal digital twins for personalized clinical decision support

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • Alexandre Vallée

摘要

Objective

This work proposes a unified framework for causal digital twins, integrating Structural Causal Models (SCMs), the Potential Outcomes Framework, and reinforcement learning. The objective is to enable individualized counterfactual reasoning and dynamic treatment policy optimization, moving beyond prediction toward actionable, ethical, and adaptive decision support.

Materials and methods

This work formalizes causal digital twins by embedding causal inference principles into digital architectures, modeling both observed and counterfactual outcomes for each patient. Individualized Treatment Effects (ITE) are estimated through causal modeling, and sequential decision-making is optimized using reinforcement learning techniques. This work is a methodological and conceptual research contribution. We propose a unified framework for the construction and evaluation of clinical digital twins and a practical checklist covering validation, safety, and ethical requirements. No new dataset or algorithmic benchmark is introduced.

Results

This work discusses methodological requirements, learning objectives, evaluation strategies, and ethical considerations necessary for deploying causal digital twins in real-world clinical contexts. The proposed framework allows for accurate estimation of counterfactual outcomes, personalized treatment effect modeling, and dynamic policy learning over time.

Discussion

Through this integration, digital twins evolve from static predictors into active engines of individualized intervention, providing a new paradigm for precision healthcare. Causal digital twins offer a transformative extension of current digital twin technologies, merging causal reasoning with dynamic simulation to enable personalized, counterfactual-driven clinical decision support.

Conclusion

This work lays the methodological foundation for operationalizing causal digital twins in future AI-driven healthcare systems, with profound implications for ethical, individualized, and adaptive medicine.