In Influence Maximization (IM), the objective is to -- given a budget -- select the optimal set of entities in a network to target with a treatment so as to maximize the total effect. For instance, in marketing, the objective is to target the set of customers that maximizes the total response rate, resulting from both direct treatment effects on targeted customers and indirect, spillover, effects that follow from targeting these customers. Recently, new methods to estimate treatment effects in the presence of network interference have been proposed. However, the issue of how to leverage these models to make better treatment allocation decisions has been largely overlooked. Traditionally, in Uplift Modeling (UM), entities are ranked according to estimated treatment effect, and the top entities are allocated treatment. Since, in a network context, entities influence each other, the UM ranking approach will be suboptimal. The problem of finding the optimal treatment allocation in a network setting is combinatorial and generally has to be solved heuristically. To fill the gap between IM and UM, we propose OTAPI: Optimizing Treatment Allocation in the Presence of Interference to find solutions to the IM problem using treatment effect estimates. OTAPI consists of two steps. First, a causal estimator is trained to predict treatment effects in a network setting. Second, this estimator is leveraged to identify an optimal treatment allocation by integrating it into classic IM algorithms. We demonstrate that this novel method outperforms classic IM and UM approaches on both synthetic and semi-synthetic datasets.

翻译：在影响力最大化（IM）问题中，目标是在给定预算约束下，选择网络中最优的实体集合进行干预，以最大化总体效应。例如在营销场景中，目标是通过选择客户群体来最大化总响应率，该响应率既包括对目标客户的直接干预效应，也包含因选择这些客户而产生的间接溢出效应。近年来，已有新方法被提出用于估计存在网络干扰时的处理效应。然而，如何利用这些模型做出更优的治疗分配决策的问题在很大程度上被忽视了。传统上，在提升建模（UM）中，实体根据估计的处理效应进行排序，排名靠前的实体被分配治疗。由于在网络环境中实体间存在相互影响，UM排序方法将无法达到最优。在网络环境中寻找最优治疗分配是一个组合优化问题，通常需要启发式求解。为填补IM与UM之间的研究空白，我们提出OTAPI：存在干扰情况下的治疗分配优化方法，利用处理效应估计求解IM问题。OTAPI包含两个步骤：首先训练一个因果估计器来预测网络环境下的处理效应；其次将该估计器整合到经典IM算法中，以识别最优治疗分配方案。我们通过合成与半合成数据集验证了该新方法在性能上优于经典IM与UM方法。