LAUSR

The article by Keil et al. (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) deploys Bayesian g- computation to investigate the causal effect of 6 airborne metal exposures linked to power plant emissions on birthweight. In so doing, it articulates the potential value of framing the analysis of 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 mixtures. Bayesian methodology such as model averaging and informative priors can help, but are ultimately limited for overcomingthis salient challenge.