Adaptive Experimentation with Delayed Binary Feedback
This addresses the problem of resource inefficiency in online experimentation for e-commerce and digital ads, though it is incremental as it adapts existing adaptive methods to handle delays.
The paper tackles the challenge of conducting experiments with delayed binary feedback, such as conversions, by proposing an adaptive method that estimates underlying objectives before they materialize and dynamically allocates variants, showing it is more efficient than other approaches in experiments.
Conducting experiments with objectives that take significant delays to materialize (e.g. conversions, add-to-cart events, etc.) is challenging. Although the classical "split sample testing" is still valid for the delayed feedback, the experiment will take longer to complete, which also means spending more resources on worse-performing strategies due to their fixed allocation schedules. Alternatively, adaptive approaches such as "multi-armed bandits" are able to effectively reduce the cost of experimentation. But these methods generally cannot handle delayed objectives directly out of the box. This paper presents an adaptive experimentation solution tailored for delayed binary feedback objectives by estimating the real underlying objectives before they materialize and dynamically allocating variants based on the estimates. Experiments show that the proposed method is more efficient for delayed feedback compared to various other approaches and is robust in different settings. In addition, we describe an experimentation product powered by this algorithm. This product is currently deployed in the online experimentation platform of JD.com, a large e-commerce company and a publisher of digital ads.