Incentive Alignment in Online Experimentation
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. B...
| Authors | Ermis Soumalias, Richard Mudd, Abbas Zaidi |
|---|---|
| Primary category | cs.GT |
| Published | 5 October 2026 |
| Last updated at source | 6 October 2026 |
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