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We present an end-to-end methodological framework for causal segmentdiscovery that aims to uncover differential impacts of treatments acrosssubgroups of users in large-scale digital experiments. Building on recentdevelopments in causal inference and non/semi-parametric statistics, ourapproach unifies two objectives: (1) the discovery of user segments that standto benefit from a candidate treatment based on subgroup-specific treatmenteffects, and (2) the evaluation of causal impacts of dynamically assigningunits to a study's treatment arm based on their predicted segment-specificbenefit or harm. Our proposal is model-agnostic, capable of incorporatingstate-of-the-art machine learning algorithms into the estimation procedure, andis applicable in randomized A/B tests and quasi-experiments. An open source Rpackage implementation, sherlock, is introduced.

A framework for causal segmentation analysis with machine learning in large-scale digital experiments