🤖 AI Summary
This study addresses the bias in causal inference within two-sided networked markets, where user interactions violate the Stable Unit Treatment Value Assumption (SUTVA). To mitigate interference effects while maintaining high node coverage, the authors propose two network-aware clustering algorithms—EgoCluster V3 and MultiEgoCluster—that leverage iterative clustering and multi-center strategies. Building upon these clusters, they integrate graph structure to enable theory-driven bias correction for average treatment effect (ATE) estimation and enhance result generalizability. Compared to existing approaches, the proposed methods reduce spillover effects by a factor of three, increase effective sample size by approximately 38%, and double statistical power. The framework has been successfully deployed in LinkedIn’s production environment, supporting high-impact experimentation at scale.
📝 Abstract
Measuring causal effects in networked two-sided marketplaces is challenging due to treatment interference between market participants on different sides. When treatment is applied to one side (e.g., job seekers), their interactions with the other side (e.g., job posters) introduce spillover effects that violate the Stable Unit Treatment Value Assumption (SUTVA) and bias causal estimates. While cluster-based randomization mitigates this problem, prior approaches struggle with a fundamental trade-off: reducing spillover requires isolated clusters that will reduce the number of qualifying clusters, which decreases statistical power. This paper introduces EgoCluster V3, an iterative clustering algorithm that reduces spillover by 3x compared to prior versions while preserving node coverage and doubling test power. We further introduce MultiEgoCluster, which extends V3 through a two-stage procedure that first groups highly connected egos into multi-ego clusters before applying the iterative clustering algorithm. This achieves an additional ~56% spillover reduction and ~38% increase in sample size. Both methods are deployed in production at LinkedIn and have systematically enabled high-impact two-sided marketplace experiments. Since residual bias cannot be fully eliminated through clustering alone, we derive a theoretical bias correction method for average treatment effect (ATE) estimation based on graph structure and propose an approach to generalize results to the general population.