Causal machine learning for exploring radiation dose effects on mandibular osteoradionecrosis

Abstract Distinguishing causal relationships from statistical correlations remains a fundamental challenge in clinical research, limiting the translation of observational findings into interventional treatment guidelines. Here we investigate whether causal machine learning can be used to explore estimated causal effects of radiation dose parameters on mandibular osteoradionecrosis (ORN), using a case study of 931 head and neck cancer patients treated with volumetric-modulated arc therapy. Using generalized random forests, all examined dosimetric factors showed positive estimated causal effects on ORN development (average treatment effects: 0.092–0.141). Integration with explainable machine learning suggested substantial treatment effect heterogeneity, with the largest estimated conditional average treatment effects observed in patients aged 50-60 years and smaller estimates in patients over 70 years. These results suggest that causal machine learning can help quantify dose-related effects and explore heterogeneity across patient characteristics. More broadly, this work provides a methodological framework for toxicity studies in oncology and other clinical settings where complex dose–response relationships warrant further prospective validation.