Direct Profit Estimation Using Uplift Modeling under Clustered Network Interference

2025-09-01

AI Summary

Traditional uplift modeling assumes no interference—violating the Stable Unit Treatment Value Assumption (SUTVA)—and thus fails to capture item-level spillover effects induced by promotional interventions in recommender systems. This paper introduces, for the first time, an interference-aware Additive Inverse Propensity Weighting (AddIPW) estimator into uplift modeling, embedding it within a differentiable gradient-based optimization framework that directly optimizes economic objectives such as incremental profit via response-variable transformation. Key contributions are: (1) explicit modeling of networked treatment spillovers; (2) end-to-end integration of causal estimators into the learning objective; and (3) profit-driven optimization of personalized incentive policies. Simulation experiments demonstrate that our method significantly outperforms conventional interference-agnostic uplift models, particularly under strong interference, enabling more accurate identification of high-value users and optimal intervention combinations, thereby enhancing policy profitability.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Uplift modeling is a key technique for promotion optimization in recommender systems, but standard methods typically fail to account for interference, where treating one item affects the outcomes of others. This violation of the Stable Unit Treatment Value Assumption (SUTVA) leads to suboptimal policies in real-world marketplaces. Recent developments in interference-aware estimators such as Additive Inverse Propensity Weighting (AddIPW) have not found their way into the uplift modeling literature yet, and optimising policies using these estimators is not well-established. This paper proposes a practical methodology to bridge this gap. We use the AddIPW estimator as a differentiable learning objective suitable for gradient-based optimization. We demonstrate how this framework can be integrated with proven response transformation techniques to directly optimize for economic outcomes like incremental profit. Through simulations, we show that our approach significantly outperforms interference-naive methods, especially as interference effects grow. Furthermore, we find that adapting profit-centric uplift strategies within our framework can yield superior performance in identifying the highest-impact interventions, offering a practical path toward more profitable incentive personalization.

Problem

• Estimating direct profit under network interference in uplift modeling
• Addressing violation of SUTVA assumption in recommendation systems
• Integrating interference-aware estimators with uplift modeling techniques

Innovation

• Uses AddIPW estimator for interference-aware uplift modeling
• Integrates differentiable learning with gradient-based optimization
• Combines profit-centric strategies with response transformation techniques