A decision support framework to implement optimal personalized marketing interventions
Personalized treatment models are critically important in decision support systems. An optimal personalized treatment maximizes the probability of a desirable outcome. We review the literature on existing methods for personalized treatment learning. We propose a new method that often outperforms the alternatives on synthetic data. We illustrate an application of the proposed method to optimize cross-selling.
marketing interventions personalized treatment learning causal inference