在线受控实验分层抽样的最优多路决策树
Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experiments
- University of Tsukuba（筑波大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对在线实验分层抽样中方差缩减效果依赖分层构建的问题，提出基于最优多路决策树的OMST方法，通过路径选择与精确方差最小化优化分层，实现更优或相当的方差缩减且保持可解释性。
AI中文摘要：
在线受控实验（即A/B测试）被广泛用于估计数字平台上的因果效应。一个核心挑战是在不增加实验样本量的情况下提高实验灵敏度（即统计功效）。分层抽样是一种经典的方差缩减技术；然而，其有效性关键取决于分层是如何构建的。因此，我们提出了一种基于优化的分层抽样框架，使用最优多路决策树。我们的方法称为最优多路分层树（OMST），将分层问题表述为特征图上的路径选择问题。所选路径定义了可解释的分层规则，并通过在连续比例分配和Neyman型最优分配下的精确方差最小化二元优化公式进行优化。我们引入监督最优分箱来为数值特征生成与结果相关的候选分割。此外，我们引入了冗余候选路径和分配约束的缩减程序，大幅减小了优化问题的规模。在真实数据集和模拟数据集上的实验表明，OMST在保持浅层且可解释的分层树的同时，实现了与现有方法相当或更优的方差缩减。
英文摘要：
Online controlled experiments, or A/B tests, are widely used to estimate causal effects on digital platforms. A central challenge is to improve experimental sensitivity, or statistical power, without increasing the experimental sample size. Stratified sampling is a classical variance reduction technique; however, its effectiveness depends critically on how the strata are constructed. We thus propose an optimization-based stratification framework for stratified sampling using optimal multi-way decision trees. Our method, called Optimal Multi-way Stratification Trees (OMST), formulates stratification as a path-selection problem over a feature graph. The selected paths define interpretable stratification rules and are optimized using an exact variance-minimizing binary optimization formulation under continuous proportional allocation and a Neyman-type optimal allocation. We incorporate supervised optimal binning to generate outcome-relevant candidate splits for numerical features. Furthermore, we introduce reduction procedures for redundant candidate paths and assignment constraints, substantially reducing the optimization problem size. Experiments on both a real-world and a simulated dataset demonstrate that OMST achieves comparable or superior variance reduction to existing methods while maintaining shallow and interpretable stratification trees.
