Mediation analysis in causal inference has traditionally centered on static interventions and binary exposures, with classical theory introducing the natural (in)direct effects thru a decomposition of the average treatment effect. From a decomposition of the population intervention effect, defined via stochastic interventions on exposure and mediators, we outline a framework for defining a variety of interesting causal contrasts, including effects for continuous and categorical exposures. Our (in)direct effects have been shown to require weaker assumptions than their average treatment effect analogs, making them a suitable choice for settings in which the cross-world independencies of traditional mediation analysis are unverifiable. We construct and evaluate two efficient estimators of our (in)direct effects: a one-step estimator and a targeted minimum loss estimator that uniquely uses an incompatible updating procedure, both of which accommodate state-of-the-art machine learning in estimating nuisance parameters. We discuss theoretical conditions for establishing the asymptotic linearity of our efficient estimators and investigate their practical performance in simulation studies.