ArticleAmerican journal of epidemiology2021
Invited Commentary: The Promise and Pitfalls of Causal Inference With Multivariate Environmental Exposures.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Everything All at Once: On Choosing an Estimand for Multi-component Environmental Exposures.Epidemiology (Cambridge, Mass.) · 2026Article
- Robust Statistical Approaches to Understanding the Causal Effect of Air Pollution Mixtures.Research report (Health Effects Institute) · 2025Article
- Causal analysis of air pollution mixtures: estimands, positivity, and extrapolation.American journal of epidemiology · 2024Article
- Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data.Current environmental health reports · 2023Review
- Synergy of Biostatistics and Epidemiology in Air Pollution Health Effects Studies.International statistical review = Revue internationale de statistique · 2022Article
- The Future of Causal Inference.American journal of epidemiology · 2022Article
- Bayesian kernel machine regression-causal mediation analysis.Statistics in medicine · 2022Article
- Keil et al. Respond to "Causal Inference for Environmental Mixtures".American journal of epidemiology · 2021Article
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Abstract
The accompanying article by Keil et al. (Am J Epidemiol. 2021;190(12):2647-2657) deploys Bayesian g-computation to investigate the causal effect of 6 airborne metal exposures linked to power-plant emissions on birth weight. In so doing, it articulates the potential value of framing the analysis of environmental mixtures as an explicit contrast between exposure distributions that might arise in response to a well-defined intervention-here, the decommissioning of coal plants. Framing the mixture analysis as that of an approximate "target trial" is an important approach that deserves incorporation into the already rich literature on the analysis of environmental mixtures. However, its deployment in the power plant example highlights challenges that can arise when the target trial is at odds with the exposure distribution observed in the data, a discordance that seems particularly difficult in studies of environmental mixtures. Bayesian methodology such as model averaging and informative priors can help, but they are ultimately limited for overcoming this salient challenge.
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Registered trials
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