  We present an end-to-end methodological framework for causal segment
discovery that aims to uncover differential impacts of treatments across
subgroups of users in large-scale digital experiments. Building on recent
developments in causal inference and non/semi-parametric statistics, our
approach unifies two objectives: (1) the discovery of user segments that stand
to benefit from a candidate treatment based on subgroup-specific treatment
effects, and (2) the evaluation of causal impacts of dynamically assigning
units to a study's treatment arm based on their predicted segment-specific
benefit or harm. Our proposal is model-agnostic, capable of incorporating
state-of-the-art machine learning algorithms into the estimation procedure, and
is applicable in randomized A/B tests and quasi-experiments. An open source R
package implementation, sherlock, is introduced.
