Inter- and Intra-Similarity Preserved Counterfactual Incentive Effect Estimation for Recommendation Systems

Personalized incentives are crucial for boosting user engagement and increasing platform revenues. Many studies have utilized uplift modeling to estimate the conditional average treatment effects (CATEs) of incentives and then allocate them under cost constraints. However, identifying which users should receive such incentives remains challenging, posing a selection bias problem. Traditional representation-based approaches mitigate bias by balancing treated and controlled distributions but overlook local similarity information. Recognizing that similar users should exhibit similar outcomes, it is vital to preserve both intra-similarity within treatment groups and inter-similarity between covariate and representation spaces. Moreover, existing methods primarily focus on CATE accuracy, neglecting the ranking ability vital for uplift modeling. We propose the Similarity Preserved Counterfactual Incentive Effect Estimation ( S-CIEE ) method, comprising three modules: (1) an Intra-Similarity Preservation Regularizer via Fused Gromov-Wasserstein Optimal Transport, (2) an Inter-Similarity Preservation Regularizer using a similarity constraint, and (3) a Rank-Aware Learning module for uplift ranking. Comprehensive experiments on one semi-synthetic and two real-world datasets show that S-CIEE improves both CATE accuracy and uplift modeling performance.