Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review.

Machine learning (ML) informs nursing practice, but prediction cannot establish intervention or workflow effects. Aim To map ML use within causal effect estimation and causal evaluations of ML-enabled interventions in nursing and midwifery. Eight electronic sources were searched for reports published January 2015-June 2026, followed by backward citation searching. Eligibility required a causal estimand, corresponding identification strategy, substantive ML role, and nursing or midwifery relevance. Two reviewers independently screened records and full texts. Fifteen studies (17 reports) were included. Five addressed acute deterioration or sepsis; others covered care prioritization, falls, post-discharge care, and nurse- or midwife-led services. Five used ML within causal effect estimation to examine longitudinal and spillover effects, treatment-effect heterogeneity, and targeting strategies. Ten evaluated ML-enabled interventions or workflows; reported favorable effects on mortality, care escalation, message-review time, and nursing processes, while several controlled estimates were null or imprecise. Evidence remains small and methodologically uneven. Future studies should align estimands, identification strategies, analytic methods, and workflows and evaluate model-plus-workflow effects on nursing processes and patient outcomes.