Integrating Causal Machine Learning into Clinical Decision Support Systems: Insights from Literature and Practice

ArXi:2603.24448v1 Announce Type: cross Current clinical decision systems (CDSSs) typically base their predictions on correlation, not causation. In recent years, causal machine learning (ML) has emerged as a promising way to improve decision-making with CDSSs by offering interpretable, treatment-specific reasoning. However, existing research often emphasizes model development rather than designing clinician-facing interfaces. To address this gap, we investigated how CDSSs based on causal ML should be designed to effectively collaborative clinical decision-making.