一种大规模异质治疗效应估计框架及其在用户旅程中的应用
A Large Scale Heterogeneous Treatment Effect Estimation Framework and Its Applications of Users' Journey at Snap
AI总结：
本文提出了一种大规模框架，用于估计用户对广告的异质治疗效应，并通过影响力评分提升关键业务指标表现。
摘要：
异质治疗效应（HTE）和条件平均治疗效应（CATE）模型放宽了治疗效果对每个用户都相同这一假设。我们提出了一种大规模工业框架，用于利用来自数亿Snapchat用户实验数据来估计HTE。通过结合多个实验的结果，该框架揭示了之前无法测量的潜在用户特征，并在大规模上产生稳定的治疗效应估计。我们描述了使该系统得以实现的核心组件，包括实验选择、基础学习器设计和增量训练。我们还突出了两个应用：用户对广告的影响力和用户对广告的敏感性。使用影响力评分进行在线A/B测试显示，关键业务指标的改进幅度超过了通常认为显著的六倍以上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
