Zalando: Causal ML in Practice: Estimating Uplift with Selection into Treatment

While experimentation is the golden standard for causal inference and is widely adopted in the industry, it is sometimes infeasible or undesirable. In these settings, a common causal estimate is the incremental impact of a feature or program that is released to the whole customer base, but only a subset of users adopts it or subscribes to it. In this talk, we present some practical learnings from the fashion industry, with an application on the incrementality of subscription programs.

• 
  Matteo Courthoud Senior Applied Scientist, Zalando