Efficient estimation of the population-level causal effects of
stochastic interventions on a continuous-valued exposure. Both one-step and
targeted minimum loss estimators are implemented for a causal parameter
defined as the counterfactual mean of an outcome of interest under a
stochastic intervention that may depend on the natural value of the
exposure (i.e., a modified treatment policy). To accommodate settings in
which two-phase sampling is employed, procedures for making use of inverse
probability of censoring weights are provided to facilitate construction of
inefficient and efficient one-step and targeted minimum loss estimators.
The causal parameter and its estimation were first described by Díaz and van
der Laan (2013)