A New Framework for Online Testing of Heterogeneous Treatment Effect
Miao Yu, Wenbin Lu, Rui Song
摘要
We propose a new framework for online testing of heterogeneous treatment effects. The proposed test, named sequential score test (SST), is able to control type I error under continuous monitoring and detect multi-dimensional heterogeneous treatment effects. We provide an online p-value calculation for SST, making it convenient for continuous monitoring, and extend our tests to online multiple testing settings by controlling the false discovery rate. We examine the empirical performance of the proposed tests and compare them with a state-of-art online test, named mSPRT using simulations and a real data. The results show that our proposed test controls type I error at any time, has higher detection power and allows quick inference on online A/B testing.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它相关 Paper
- YEAST: Yet Another Sequential TestAlexey Kurennoy, Majed Dodin, Tural Gurbanov, Ana Peleteiro-RamalloNeurIPS 2025
- Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision MakingTing Li, Chengchun Shi, Jianing Wang, Fan Zhou 等NeurIPS 2023 · 被引用 21 次
- SCORE: A Unified Framework for Overshoot Refund in Online FDR ControlQi Kuang, Bowen Gang, Yin XiaICML 2026 · 被引用 2 次
- Anytime-Valid Inference For Multinomial Count DataMichael Lindon, Alan MalekNeurIPS 2022 · 被引用 26 次
- Anytime Detection of Strategic Deviations in Multi-Agent SystemsEtienne Gauthier, Francis Bach, Michael JordanICML 2026 · 被引用 2 次
