  Understanding treatment effect heterogeneity has become an increasingly
popular task in various fields, as it helps design personalized advertisements
in e-commerce or targeted treatment in biomedical studies. However, most of the
existing work in this research area focused on either analyzing observational
data based on strong causal assumptions or conducting post hoc analyses of
randomized controlled trial data, and there has been limited effort dedicated
to the design of randomized experiments specifically for uncovering treatment
effect heterogeneity. In the manuscript, we develop a framework for designing
and analyzing response adaptive experiments toward better learning treatment
effect heterogeneity. Concretely, we provide response adaptive experimental
design frameworks that sequentially revise the data collection mechanism
according to the accrued evidence during the experiment. Such design strategies
allow for the identification of subgroups with the largest treatment effects
with enhanced statistical efficiency. The proposed frameworks not only unify
adaptive enrichment designs and response-adaptive randomization designs but
also complement A/B test designs in e-commerce and randomized trial designs in
clinical settings. We demonstrate the merit of our design with theoretical
justifications and in simulation studies with synthetic e-commerce and clinical
trial data.
