Optimizing Treatment Allocation in the Presence of Interference

2024-09-30

arXiv.org

AI Summary

This paper addresses the budget-constrained optimal treatment allocation problem under network interference, where the goal is to select a set of intervention nodes that maximizes the sum of direct and local neighborhood spillover effects. We propose the two-stage Optimal Treatment Allocation via Propagation and Influence (OTAPI) framework—the first systematic integration of causal uplift modeling and influence maximization (IM), overcoming the suboptimality of conventional ranking-based allocation methods in interference-prone settings. Our approach employs a network-aware causal inference estimator for heterogeneous treatment effect estimation, explicitly modeling both individual responsiveness and localized spillovers, while remaining compatible with standard IM heuristics (e.g., Greedy, IMM). Experiments on synthetic and semi-synthetic datasets demonstrate an average 12.7% improvement in total response rate over classical IM and uplift modeling baselines, validating the efficacy and practicality of joint causal-structural modeling for social and healthcare interventions.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Search and Optimization: Algorithm Configuration

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 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.

Problem

• Optimizing treatment allocation under network interference effects
• Bridging gap between Influence Maximization and Uplift Modeling
• Solving NP-hard network treatment allocation heuristically

Innovation

• Uses causal estimator for network treatment effects
• Integrates estimator into Influence Maximization algorithms
• Outperforms classic IM and UM approaches