Understanding cause and effect relationships in Spotify data to inform decision-making is crucial for best serving Spotify’s users and the company. The generally-accepted gold standard solution to this problem is to conduct a randomised controlled trial, or A/B test. However, in many situations such trials cannot be performed; they could be unethical, exorbitantly expensive, or technologically infeasible. In the absence of such trials, many methods have been developed to infer the causal impact of an intervention or treatment from observational data given certain assumptions. One of the most widely used causal inference approaches in economics, marketing, and medicine are synthetic control methods.
