Journey to the Centre of Cluster: Harnessing Interior Nodes for A/B Testing under Network Interference

2026-02-04

AI Summary

This work addresses the challenges of A/B testing under network interference, where conventional estimators suffer from high variance due to data trimming and bias arising from unrepresentative internal nodes. To mitigate these issues, the authors propose the Mean-Internal-Internal (MII) estimator, which reduces variance by focusing on internal nodes within clusters and corrects for covariate distribution shifts using a counterfactual predictor trained on the entire network to alleviate bias. The approach further uncovers an intrinsic connection between MII and prediction-augmented inference frameworks, framing selective bias correction from a semi-supervised learning perspective. Extensive experiments across diverse and complex simulated scenarios demonstrate that the enhanced MII estimator significantly improves estimation accuracy while maintaining low variance, outperforming existing methods.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

A/B testing on platforms often faces challenges from network interference, where a unit's outcome depends not only on its own treatment but also on the treatments of its network neighbors. To address this, cluster-level randomization has become standard, enabling the use of network-aware estimators. These estimators typically trim the data to retain only a subset of informative units, achieving low bias under suitable conditions but often suffering from high variance. In this paper, we first demonstrate that the interior nodes - units whose neighbors all lie within the same cluster - constitute the vast majority of the post-trimming subpopulation. In light of this, we propose directly averaging over the interior nodes to construct the mean-in-interior (MII) estimator, which circumvents the delicate reweighting required by existing network-aware estimators and substantially reduces variance in classical settings. However, we show that interior nodes are often not representative of the full population, particularly in terms of network-dependent covariates, leading to notable bias. We then augment the MII estimator with a counterfactual predictor trained on the entire network, allowing us to adjust for covariate distribution shifts between the interior nodes and full population. By rearranging the expression, we reveal that our augmented MII estimator embodies an analytical form of the point estimator within prediction-powered inference framework. This insight motivates a semi-supervised lens, wherein interior nodes are treated as labeled data subject to selection bias. Extensive and challenging simulation studies demonstrate the outstanding performance of our augmented MII estimator across various settings.

Problem

• network interference
• A/B testing
• cluster-level randomization
• interior nodes
• covariate shift

Innovation

• network interference
• cluster-level randomization
• interior nodes
• prediction-powered inference
• semi-supervised estimation