Incentive Alignment in Online Experimentation

2026-10-05

AI Summary

This study addresses the incentive conflicts among agents arising from the decentralization of online experiments, which lead to upward bias in average treatment effect (ATE) estimation and diminish platform value. By pioneering a game-theoretic perspective, this work reformulates experimentation as an incentive design problem and proposes a governance framework that requires no additional traffic. Specifically, it integrates sample splitting with Bayesian shrinkage estimation to align multi-party interests, thereby overcoming the limitations of traditional centralized regulation. The proposed approach achieves perfect incentive alignment, ensuring that negative interventions remain strictly unprofitable and effectively eliminating the upward bias in experimental outcomes.

Abstract

Evaluating the causal effect of new features is a central goal for online platforms. While recent literature addresses limited testing traffic via centralized portfolio optimization, this perspective abstracts away a critical institutional reality: experimentation is operationally decentralized. The experimenters who develop new features also dictate which hypotheses to test, and they are typically rewarded based on empirical average treatment effects that are prone to upward bias. Left unchecked, this principal-agent conflict can severely erode platform value, a structural failure that conventional centralized levers, such as significance thresholds and traffic budgets, cannot resolve. By reframing experimentation as an incentive design problem, we demonstrate that two practical mechanisms, sample splitting and shrinkage, can effectively bridge this gap. Sample splitting aligns incentives perfectly at a bounded traffic cost, while shrinkage consumes no additional traffic and guarantees that interventions with negative expected effects are strictly unprofitable to field.

Problem

• Incentive Alignment
• Online Experimentation
• Principal-Agent Conflict
• Upward Bias
• Decentralized Experimentation

Innovation

• Incentive Alignment
• Online Experimentation
• Sample Splitting
• Shrinkage
• Principal-Agent Problem