Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experiments
This work proposes an optimization-based stratification framework for stratified sampling using optimal multi-way decision trees, and introduces reduction procedures for redundant candidate paths and assignment constraints, substantially reducing the optimization problem size.
Tomoka Takei, Shunnosuke Ikeda, Yuichi Takano
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