  Interventional effects for mediation analysis were proposed as a solution to
the lack of identifiability of natural (in)direct effects in the presence of a
mediator-outcome confounder affected by exposure. We present a theoretical and
computational study of the properties of the interventional (in)direct effect
estimands based on the efficient influence fucntion (EIF) in the non-parametric
statistical model. We use the EIF to develop two asymptotically optimal,
non-parametric estimators that leverage data-adaptive regression for estimation
of the nuisance parameters: a one-step estimator and a targeted minimum loss
estimator. A free and open source \texttt{R} package implementing our proposed
estimators is made available on GitHub. We further present results establishing
the conditions under which these estimators are consistent, multiply robust,
$n^{1/2}$-consistent and efficient. We illustrate the finite-sample performance
of the estimators and corroborate our theoretical results in a simulation
study. We also demonstrate the use of the estimators in our motivating
application to elucidate the mechanisms behind the unintended harmful effects
that a housing intervention had on adolescent girls' risk behavior.
