We propose a causal predictive framework for estimating risk under preventative interventions. The Unexposed Mediator Model maintains mediators that are also predictors at their unexposed level, removing double counting of intervention effects at followup visits. The Modifiable Risk Factor Model handles multiple interventions flexibly by modelling their effects via mediators that are also predictors, assuming a known causal structure. The Two Component Model combines a predictive baseline model with an intervention model to improve predictive performance. We illustrate the framework in primary prevention of cardiovascular disease. The proposed models allow arbitrary interventions to be evaluated within a prediction under intervention framework, with causally consistent risk estimates across repeated visits. Limitations include reliance on predictor values from an arbitrary first visit, requirements for causal structural knowledge, and a consistency assumption, that interventions with identical effects on predictors have identical effects on outcomes, which warrant further investigation.

翻译：我们提出了一种用于估计预防性干预下风险的因果预测框架。未暴露中介模型将同时作为预测因子的中介变量维持在未暴露水平，从而避免了随访期间干预效应的重复计算。可修改风险因子模型通过将干预效应建模为同时作为预测因子的中介变量，灵活处理多重干预，该模型假设已知因果结构。双组件模型将预测基线模型与干预模型相结合以提升预测性能。我们在心血管疾病一级预防中展示了该框架的应用。所提出的模型允许在干预预测框架内评估任意干预措施，并在重复随访中获得因果一致的的风险估计。局限性包括：依赖任意首次就诊的预测因子数值、需要因果结构知识、以及一致性假设（即对预测因子具有相同效应的干预措施对结局也具有相同效应），这些都需要进一步研究。