This paper employs a causal inference framework to explore two logically distinct forms of preventive effect heterogeneity relevant for studying variation in preventive effect as a basis for developing more personalized interventions. Following VanderWeele (2015), I begin with a discussion of causal interaction involving manipulable moderators that combine to yield more complex nonadditive effects. This is contrasted with effect heterogeneity, which involves variation in causal structure indexed by stable characteristics of populations or contexts. The paper then discusses one particularly promising approach, the baseline target moderated mediation (BTMM) design, which uses theoretically informed baseline target moderators to strengthen causal inference, suggesting methods for using BTMM designs to develop targeting strategies for personalized prevention. It presents examples of recent intervention trials that apply these different forms of moderation, and discusses causal inference and the problem of moderation confounding, reviewing methods for minimizing its impact, including recent advances in the use of propensity score matching.
Keywords: Causal inference; Moderation; Personalized prevention; Research design.