Decomposing a treatment effect on an outcome into separate path-specific effects via multiple mediators requires strict assumptions, such as correctly postulating the mediators' causal structure and no unmeasured confounding among the mediators. In contrast, interventional indirect effects for multiple mediators can be identified even when these assumptions are violated. Existing estimation methods require calculating each distinct interventional indirect effect in turn. This can quickly become unwieldy when investigating effects modified by observed baseline covariates. In this article, we introduce simplified estimation methods for such heterogeneous interventional indirect effects using interventional effect models. Interventional effect models are a class of marginal structural models that encode the interventional indirect effects as causal parameters, thus readily permitting effect modification using (statistical) interaction terms. The mediators and outcome can be continuous or noncontinuous. We propose two estimation procedures: one using inverse weighting by the counterfactual mediator density or mass functions, and another using Monte Carlo integration.