Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experiments

arXiv:2609.23308 · cs.LG, stat.CO · Submitted 2026-09-20 · Read on arXiv

cs.LG, stat.CO

Submitted: 2026-09-20

Updated: 2026-09-20

Comments: 16 pages, 4 figures, The 23rd Pacific Rim International Conference on Artificial Intelligence 2026 (PRICAI 2026)

Code: https://github.com/t087t/optimal-multiwaystratification-trees

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 10× in this paper · explained in 2 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Interpretability — 5× in this paper · explained in 15 episodes
• Optimal Multi-way Stratification Trees — 5× in this paper
• Pruning — 4× in this paper · explained in 4 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Controlled Experiments Using Pre-Experiment Data — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Local Optimization — 1× in this paper · explained in 2 episodes
• Optimal Multiwaysplit Decision Trees — 1× in this paper

Sources

• Improving Sensitivity in A/B Tests: Integrating CUPED with Trimmed Mean Techniques
• Optimal binning: mathematical programming formulation
• Interpretable clustering via optimal multiway-split decision trees

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