Heteroscedasticity-aware stratified sampling to improve uplift modeling
考虑异方差的分层抽样：改进增益建模AI 解读 ↓
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考虑异方差的分层抽样：改进增益建模
- 研究问题
- 在成本随样本规模增加的随机对照试验中，能否利用个体结果噪声的差异安排抽样，提高平均及异质性治疗效应估计精度？
- 方法与思路
- 作者提出考虑异方差的分层抽样HS，对高噪声个体配置更多观测，并用理论与经验实验同完全随机抽样的试验数据比较。
- 材料陈述的贡献
- 摘要称HS抽样提高平均治疗效应估计精度、改善增益模型，并使模型评价更可靠，为有限实验预算下的样本配置提供方案。
- 阅读关注点
- 应核对噪声水平如何预先估计、分层与样本分配规则，以及不等抽样概率的校正、成本约束和模型评价程序如何设计。
- 阅读边界
- 这是预印本，同行评审及期刊接收未获核验。摘要未说明理论条件、噪声估计误差或改善幅度，收益仍需结合具体实验设计判断。
原文摘要
In many business applications, including online marketing and customer churn prevention, randomized controlled trials (RCT's) are conducted to investigate on the effect of specific treatment (coupon offers, advertisement mailings,...). Such RCT's allow for the estimation of average treatment effects as well as the training of (uplift) models for the heterogeneity of treatment effects between individuals. The problem with these RCT's is that they are costly and this cost increases with the number of individuals included into the RCT. For this reason, there is research how to conduct experiments involving a small number of individuals while still obtaining precise treatment effect estimates. We contribute to this literature a heteroskedasticity-aware stratified sampling (HS) scheme, which leverages the fact that different individuals have different noise levels in their outcome and precise treatment effect estimation requires more observations from the "high-noise" individuals than from the "low-noise" individuals. By theory as well as by empirical experiments, we demonstrate that our HS-sampling yields significantly more precise estimates of the ATE, improves uplift models and makes their evaluation more reliable compared to RCT data sampled completely randomly. Due to the relative ease of application and the significant benefits, we expect HS-sampling to be valuable in many real-world applications.