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; moreover, we discuss extensions of our proposed effects to settings featuring intermediate confounders. We construct and evaluate efficient estimators of our (in)direct effects, all of which accommodate state-of-the-art machine learning in the estimation of nuisance parameters. We discuss theoretical conditions for establishing the asymptotic linearity of our efficient estimators and investigate their practical performance in simulation studies. This is based on joint work with Iván Díaz and Mark van der Laan, based on https://doi.org/10.1111/rssb.12362 and https://arxiv.org/abs/2009.06203.