Machine Learning (ML) is gaining popularity in epidemiology and healthcare studies for hypothesis-free discovery of risk and protective factors. ML is strong at discovering nonlinearities and interactions, but this power is compromised by a lack of reliable inference. Although Shapley values provide local measures of features' effects, valid uncertainty quantification for these effects is typically lacking, thus precluding statistical inference. We propose RuleSHAP, a framework that addresses this limitation by combining a dedicated Bayesian sparse regression model with an improved tree-based rule generator and Shapley value attribution. RuleSHAP provides detection of nonlinear and interaction effects, with uncertainty quantification at the individual level as a key contribution. We derive an efficient formula for computing marginal Shapley values within this framework. We apply RuleSHAP to data from an epidemiological cohort to detect and infer several effects for high cholesterol and blood pressure, such as nonlinear interaction effects between features like age, sex, ethnicity, BMI and glucose level. To conclude, we demonstrate the validity of our framework on simulated data.

翻译：机器学习（ML）在流行病学和医疗健康研究中日益流行，用于无假设发现风险因素与保护因素。ML擅长发现非线性关系与交互作用，但这一能力因缺乏可靠推断而受限。尽管Shapley值能提供变量效应的局部度量，但这些效应通常缺乏有效的不确定性量化，从而阻碍了统计推断。我们提出RuleSHAP框架，通过将专用贝叶斯稀疏回归模型与改进的基于树的规则生成器及Shapley值归因相结合，解决了这一局限。RuleSHAP能够检测非线性效应与交互效应，其关键贡献在于实现了个体层面的不确定性量化。我们推导了在该框架内计算边际Shapley值的高效公式。将RuleSHAP应用于某流行病学队列数据，检测并推断出高胆固醇与血压的若干效应，例如年龄、性别、种族、BMI及血糖水平等变量间的非线性交互效应。最后，我们通过模拟数据验证了该框架的有效性。