基于数亿用户数据，构建可扩展的异质处理效应估计框架。
A Large Scale Heterogeneous Treatment Effect Estimation Framework and Its Applications of Users' Journey at Snap
- 融合海量实验数据，通过增量训练与模型设计实现高效估计
- 识别出此前不可测的用户潜藏特征，稳定估算个体化处理效应
- 适用于广告影响度与敏感度分析，助力精准投放
异质处理效应（HTE）与条件平均处理效应（CATE）模型放宽了处理效应对所有用户相同的假设。本文提出一个大规模工业级框架，利用来自数亿Snapchat用户的实验数据估计HTE。通过整合多个实验结果，该框架揭示了此前无法测量的潜在用户特征，并实现了大规模下的稳定处理效应估计。文中介绍了系统核心组件，包括实验选择、基础学习器设计和增量训练机制。还展示了两个应用场景：用户对广告的影响能力评估与敏感度分析。一项在线A/B测试显示，使用影响能力评分进行定向投放，关键业务指标提升超过通常显著性水平的六倍。
原文摘要 · Abstract (English)
Heterogeneous Treatment Effect (HTE) and Conditional Average Treatment Effect (CATE) models relax the assumption that treatment effects are the same for every user. We present a large scale industrial framework for estimating HTE using experimental data from hundreds of millions of Snapchat users. By combining results across many experiments, the framework uncovers latent user characteristics that were previously unmeasurable and produces stable treatment effect estimates at scale. We describe the core components that enabled this system, including experiment selection, base learner design, and incremental training. We also highlight two applications: user influenceability to ads and user sensitivity to ads. An online A/B test using influenceability scores for targeting showed an improvement on key business metrics that is more than six times larger than what is typically considered significant.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。