  Black-box heterogeneous treatment effect (HTE) models are increasingly being
used to create personalized policies that assign individuals to their optimal
treatments. However, they are difficult to understand, and can be burdensome to
maintain in a production environment. In this paper, we present a scalable,
interpretable personalized experimentation system, implemented and deployed in
production at Meta. The system works in a multiple treatment, multiple outcome
setting typical at Meta to: (1) learn explanations for black-box HTE models;
(2) generate interpretable personalized policies. We evaluate the methods used
in the system on publicly available data and Meta use cases, and discuss
lessons learnt during the development of the system.
