Many research questions -- particularly those in environmental health -- do not involve binary exposures. In environmental epidemiology, this includes multivariate exposure mixtures with nondiscrete components. Causal inference estimands and estimators to quantify the relationship between an exposure mixture and an outcome are relatively few. We propose an approach to quantify a relationship between a shift in the exposure mixture and the outcome -- either in the single timepoint or longitudinal setting. The shift in the exposure mixture can be defined flexibly in terms of shifting one or more components, including examining interaction between mixture components, and in terms of shifting the same or different amounts across components. The estimand we discuss has a similar interpretation as a main effect regression coefficient. First, we focus on choosing a shift in the exposure mixture supported by observed data. We demonstrate how to assess extrapolation and modify the shift to minimize reliance on extrapolation. Second, we propose estimating the relationship between the exposure mixture shift and outcome completely nonparametrically, using machine learning in model-fitting. This is in contrast to other current approaches, which employ parametric modeling for at least some relationships, which we would like to avoid because parametric modeling assumptions in complex, nonrandomized settings are tenuous at best. We are motivated by longitudinal data on pesticide exposures among participants in the CHAMACOS Maternal Cognition cohort. We examine the relationship between longitudinal exposure to agricultural pesticides and risk of hypertension. We provide step-by-step code to facilitate the easy replication and adaptation of the approaches we use.

翻译：许多研究问题——尤其是环境健康领域的问题——并不涉及二元暴露。在环境流行病学中，这包括具有非离散组分的多元暴露混合物。用于量化暴露混合物与结果之间关系的因果推断估计量与估计方法相对较少。我们提出一种方法，用于量化暴露混合物变化与结果之间的关系——无论是在单时间点还是纵向研究设定中。暴露混合物的变化可以灵活定义，包括移动一个或多个组分（含组分间交互作用检验），以及各组分移动相同或不同量值。我们讨论的估计量具有与主效应回归系数类似的解释。首先，我们关注选择观测数据支持的暴露混合物变化量。我们演示如何评估外推并修改变化量以最小化对外推的依赖。其次，我们提出完全非参数地估计暴露混合物变化与结果之间的关系，在模型拟合中使用机器学习方法。这与当前其他方法形成对比——那些方法至少对某些关系采用参数化建模，而我们希望避免这种做法，因为在复杂的非随机化设定中，参数化建模假设最多只是脆弱的。我们的研究动机来自CHAMACOS母亲认知队列参与者农药暴露的纵向数据。我们检验农业农药的纵向暴露与高血压风险之间的关系。我们提供逐步代码以促进所用方法的便捷复现与适配。