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

  • Early Life Environmental Health (H Volk and J Buckley, Section Editors)
  • Published:
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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.

Summary

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.

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Funding

TJSS was supported by the National Institute for Environmental Health Sciences (NIEHS; T32ES007141). APK and JPB were supported by NIEHS (R01ES029531).

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Authors and Affiliations

  1. Department of Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Tyler J. S. Smith

  2. Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, USA

    Alexander P. Keil

  3. Departments of Environmental Health & Engineering and Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, W7515, Baltimore, MD, 21205, USA

    Jessie P. Buckley

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  1. Tyler J. S. Smith
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Correspondence to Jessie P. Buckley.

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Smith, T.J.S., Keil, A.P. & Buckley, J.P. Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data. Curr Envir Health Rpt 10, 12–21 (2023). https://doi.org/10.1007/s40572-022-00388-y

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