Optimizing Treatment Allocation in the Presence of Interference

TL;DR

This paper introduces OTAPI, a new method that combines causal effect estimation with influence maximization algorithms to optimize treatment allocation in networks with interference, outperforming traditional methods.

Contribution

The paper proposes OTAPI, a novel approach that integrates treatment effect estimation into influence maximization algorithms for better network treatment allocation.

Findings

OTAPI outperforms classic influence maximization methods.

OTAPI surpasses uplift modeling in network treatment scenarios.

The method is effective on synthetic and semi-synthetic datasets.

Abstract

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 \textcolor{red}{NP-hard,} 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.