From prediction to intervention: Causal artificial intelligence as the missing link in precision therapeutics

Abstract Artificial intelligence (AI) applied to multiomics data has expanded precision medicine's capacity to predict disease risk, prognosis, and treatment response. However, predictive accuracy alone does not answer the central question of precision therapeutics: which intervention will improve a given patient's outcome. Conventional multiomics models primarily capture statistical associations and remain vulnerable to confounding and distributional shift, limiting clinical actionability. Causal artificial intelligence (causal AI), defined here as the integration of causal inference with machine learning (ML) to estimate intervention effects rather than predict outcomes, offers a complementary framework. This review critically examines the conceptual foundations, terminology, methodological maturity, and translational status of causal AI in precision therapeutics, distinguishing causal inference, causal ML, causal discovery, and causal representation learning, and emphasizing multiomics integration and biological mechanisms. I review causal forests, targeted maximum likelihood estimation, Mendelian randomization, and target trial emulation, alongside layer‐specific opportunities across multiomics data. Emerging applications in oncology, cardiometabolic disease, and psychiatry illustrate the potential of causal AI to characterize treatment‐effect heterogeneity, though most remain at hypothesis‐generating or retrospective stages, and methodological, interpretability, regulatory, and equity challenges remain substantial. Mechanism‐anchored causal AI offers a promising but still immature path toward closing the gap between molecular prediction and clinical intervention.