Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data

Abstract

Purpose of review: We discuss how epidemiologic studies have used observational data to estimate the effects of potential interventions on early-life environmental exposures. We summarize the value of posing questions about interventions, how a group of techniques known as "g-methods" can provide advantages for estimating intervention effects, and how investigators have grappled with the strong assumptions required for causal inference.

Recent findings: We identified nine studies that estimated health effects of hypothetical interventions on early-life environmental exposures. Of these, six examined air pollution. Interventions evaluated by these studies included setting exposure levels at a specific value, shifting exposure distributions, and limiting exposure levels to less than a threshold value. Only one study linked exposure contrasts to a specific intervention on an exposure source, however. There is growing interest in estimating intervention effects of early-life environmental exposures, in part because intervention effects are directly related to possible public health actions. Future studies can build on existing work by linking research questions to specific hypothetical interventions that could reduce exposure levels. We discuss how framing questions around interventions can help overcome some of the barriers to causal inference and how advances related to machine learning may strengthen studies by sidestepping the overly restrictive assumptions of parametric regression models. By leveraging advancements in causal inference and exposure science, an intervention framework for environmental epidemiology can guide actionable solutions to improve children's environmental health.

Keywords: Air pollution; Causal inference; Children’s health; Environmental epidemiology; Pregnancy; Prenatal exposures.

Fig. 1

Examples of hypothetical interventions. a Set the exposure at a specific value b Shift the exposure c Limit the exposure to less than or equal to a threshold value. The dashed red curve indicates the distribution of exposure values assigned under the intervention. The solid blue curve indicates the distribution of exposure values assigned under the natural course.