Causal mediation analysis examines causal pathways linking exposures to
disease. The estimation of interventional effects, which are mediation
estimands that overcome certain identifiability problems of natural effects,
has been advanced through causal machine learning methods, particularly for
high-dimensional mediators. Recently, it has been proposed interventional
effects can be defined in each study by mapping to a target trial assessing
specific hypothetical mediator interventions. This provides an appealing
framework to directly address real-world research questions about the extent to
which such interventions might mitigate an increased disease risk in the
exposed. However, existing estimators for interventional effects mapped to a
target trial rely on singly-robust parametric approaches, limiting their
applicability in high-dimensional settings. Building upon recent developments
in causal machine learning for interventional effects, we address this gap by
developing causal machine learning estimators for three interventional effect
estimands, defined by target trials assessing hypothetical interventions
inducing distinct shifts in joint mediator distributions. These estimands are
motivated by a case study within the Longitudinal Study of Australian Children,
used for illustration, which assessed how intervening on high inflammatory
burden and other non-inflammatory adverse metabolomic markers might mitigate
the adverse causal effect of overweight or obesity on high blood pressure in
adolescence. We develop one-step and (partial) targeted minimum loss-based
estimators based on efficient influence functions of those estimands,
demonstrating they are root-n consistent, efficient, and multiply robust under
certain conditions.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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