Our research project aims to optimize the value and outcomes of palliative radiation therapy (PRT) for bone metastases in advanced breast and lung cancers by linking real-world practice patterns to individualized effectiveness and economic value. First, we will evaluate multi-level factors—spanning patient, clinician, facility, and health system contexts—associated with the selection and delivery of different PRT approaches. Second, we will apply causal machine learning to estimate heterogeneous treatment effects and identify which patients are most likely to benefit from advanced versus conventional PRT strategies. Third, we will integrate these individualized treatment effects into patient-level microsimulation models to quantify cost-effectiveness and distributional cost-effectiveness, generating actionable evidence on both overall value and equity implications of alternative PRT approaches.