Unlike classical causal inference, where the goal is to estimate average causal effects within a population, in settings such as personalized medicine, the goal is to map a unit's characteristics to a treatment tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic treatment regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway is often of interest as well. In the context of average treatment effects, estimation of effects associated with causal pathways is considered in the mediation analysis literature. In this paper, we combine mediation analysis and dynamic treatment regime ideas and consider how unit characteristics may be used to tailor a treatment strategy that maximizes an effect along specified sets of causal pathways. In particular, we define counterfactual responses to such policies, give a general identification algorithm for these counterfactuals, and prove completeness of the algorithm for unrestricted policies. A corollary of our results is that the identification algorithm for responses to policies given in [16] is complete for arbitrary policies.