There are many situations at Spotify where A/B tests can’t be performed: they could be too technically complex to implement, too damaging to the user experience, or they could infringe on a contractual agreement we have with a customer, to name a few. When A/B tests can’t be run, teams can’t measure the success or impact of their product or new release—potentially leading to a “ship and pray” situation.

Spotify_Research's publication is a hub for academic research and industry insights in the field of music streaming technology and user behavior analysis. Through research papers, data analysis, and experimental studies, Spotify_Research offers  insights into music recommendation algorithms, user engagement metrics, and content discovery mechanisms. Readers can learn about the latest advancements in music streaming technology, personalized playlist generation, and user-centric design principles to enhance the music listening experience. Additionally, Spotify_Research provides data-driven insights, user studies, and market analysis to help music enthusiasts and industry professionals understand the evolving landscape of digital music consumption and anticipate future trends.

Spotify Research

Spotify Research introduces an improved synthetic control methodology for measuring product impact when A/B testing isn't feasible. The approach uses forecasting to automatically detect spillover effects and select valid donor controls, eliminating reliance on domain knowledge alone. A new theorem based on proximal causal inference enables data-driven donor selection by testing if pre-intervention forecasts match post-intervention reality. The method also debiases estimates by leveraging excluded donors' pre-intervention data, providing sensitivity analysis frameworks to handle selection errors and latent confounding.

Spillover Detection for Donor Selection in Synthetic Control Models