Synthesizing Evidence: Data-Pooling as a Tool for Treatment Selection in Online Experiments
综合证据：利用在线实验数据汇集选择处理方案AI 解读 ↓
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综合证据：利用在线实验数据汇集选择处理方案
- 研究问题
- 在线平台实验流量有限、处理效应异质且实验可能重叠时，如何通过跨实验汇集数据降低估计波动，并改善处理方案部署？
- 方法与思路
- 作者提出数据汇集处理部署框架，将多个实验的信息用于策略选择，涵盖重叠与非重叠流量及线性、非线性模型设定。
- 材料陈述的贡献
- 摘要称，理论比较在非重叠实验中显示其优势，并用复杂场景模拟和两个平台实验数据集展示子群与跨实验的策略部署效果。
- 阅读关注点
- 应核对跨实验信息可共享的条件、实验重叠时的处理交互、理论比较采用的损失指标，以及策略选择是否独立于效果评价。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收均未核实；摘要未说明理论优势的具体条件，重叠和非线性情形主要由模拟与应用支持。
原文摘要
Randomized experiments are the gold standard for causal inference but face significant challenges in business applications, including limited traffic allocation, the need for heterogeneous treatment effect estimation, and the complexity of managing overlapping experiments. These factors lead to high variability in treatment effect estimates, making data-driven policy roll out difficult. To address these issues, we introduce the data pooling treatment roll-out (DPTR) framework, which enhances policy roll-out by pooling data across experiments rather than focusing narrowly on individual ones. DPTR can effectively accommodate both overlapping and non-overlapping traffic scenarios, regardless of linear or nonlinear model specifications. We demonstrate the framework's robustness through a three-pronged validation: (a) theoretical analysis shows that DPTR surpasses the traditional difference-in-mean and ordinary least squares methods under non-overlapping experiments, particularly when the number of experiments is large; (b) synthetic simulations confirm its adaptability in complex scenarios with overlapping traffic, rich covariates and nonlinear specifications; and (c) empirical applications to two experimental datasets from real world platforms, demonstrating its effectiveness in guiding customized policy roll-outs for subgroups within a single experiment, as well as in coordinating policy deployments across multiple experiments with overlapping scenarios. By reducing estimation variability to improve decision-making effectiveness, DPTR provides a scalable, practical solution for online platforms to better leverage their experimental data in today's increasingly complex business environments.