  Understanding the pathways whereby an intervention has an effect on an
outcome is a common scientific goal. A rich body of literature provides various
decompositions of the total intervention effect into pathway specific effects.
Interventional direct and indirect effects provide one such decomposition.
Existing estimators of these effects are based on parametric models with
confidence interval estimation facilitated via the nonparametric bootstrap. We
provide theory that allows for more flexible, possibly machine learning-based,
estimation techniques to be considered. In particular, we establish weak
convergence results that facilitate the construction of closed-form confidence
intervals and hypothesis tests. Finally, we demonstrate multiple robustness
properties of the proposed estimators. Simulations show that inference based on
large-sample theory has adequate small-sample performance. Our work thus
provides a means of leveraging modern statistical learning techniques in
estimation of interventional mediation effects.
