研究广告系统中干预对长期收益的影响，提出新方法估计因果效应。
Causal Inference on Stopped Random Walks in Online Advertising
- 将广告实验建模为停止的随机游走，考虑用户行为和预算约束的动态变化
- 通过预算拆分设计与统计定理，构建长期收益的置信区间
- 适合在线广告优化、因果推断研究者参考
我们研究在线广告系统中常见的因果推断问题：平台（如Instagram、TikTok）反复与用户和广告商互动，通过拍卖选择性展示广告。每个处理对应广告机制参数（如拍卖底价），目标是通过实验估计其长期影响（如年广告收入）。在该设定下，处理不仅影响即时广告收入，还改变用户交互轨迹及广告商出价策略——后者受有限预算约束。尤其值得注意的是，处理可能影响用户留存，从而改变总体用户规模。我们放弃传统的独立同分布假设，将实验观测值（如广告收入）建模为停止的随机游走，采用预算拆分实验设计，结合Anscombe定理、Wald型方程及中心极限定理，构造长期处理效应的置信区间。
原文摘要 · Abstract (English)
We consider a causal inference problem frequently encountered in online advertising systems, where a publisher (e.g., Instagram, TikTok) interacts repeatedly with human users and advertisers by sporadically displaying to each user an advertisement selected through an auction. Each treatment corresponds to a parameter value of the advertising mechanism (e.g., auction reserve-price), and we want to estimate through experiments the corresponding long-term treatment effect (e.g., annual advertising revenue). In our setting, the treatment affects not only the instantaneous revenue from showing an ad, but also changes each user's interaction-trajectory, and each advertiser's bidding policy -- as the latter is constrained by a finite budget. In particular, each a treatment may even affect the size of the population, since users interact longer with a tolerable advertising mechanism. We drop the classical i.i.d. assumption and model the experiment measurements (e.g., advertising revenue) as a stopped random walk, and use a budget-splitting experimental design, the Anscombe Theorem, a Wald-like equation, and a Central Limit Theorem to construct confidence intervals for the long-term treatment effect.
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