Everything all at once: On choosing an estimand for multi-component environmental exposures

2025-09-22

AI Summary

In environmental health, causal inference on multicomponent non-discrete exposure mixtures—such as pesticide cocktails—and chronic diseases (e.g., hypertension) has long been hindered by the absence of interpretable and robust estimation frameworks. To address this, we propose a flexible, fully nonparametric causal estimation method. Interpretability is formally defined via mixture exposure shift parameters representing main effects, enabling joint modeling of multi-component shifts and interaction analysis. Robustness is enhanced through a data-driven extrapolation-constraint strategy, and the framework accommodates both cross-sectional and longitudinal study designs. Applied to the CHAMACOS cohort, our method uncovers statistically significant associations between dynamic changes in pesticide mixture exposures and hypertension risk. An open-source implementation ensures reproducibility and facilitates broader adoption in environmental epidemiology and causal inference research.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Quantifying relationships between multi-component environmental exposures and health outcomes
• Developing nonparametric causal inference methods for exposure mixture shifts
• Assessing pesticide exposure effects on hypertension risk using longitudinal data

Innovation

• Flexible shift definition for exposure mixtures
• Nonparametric estimation using machine learning
• Minimizing extrapolation through data-supported shifts