在线广告中停止随机游走的因果推断
Causal Inference on Stopped Random Walks in Online Advertising
AI总结：
本研究通过停止随机游走模型和预算分割设计，构建在线广告中长期处理效应的置信区间。
摘要：
我们考虑在线广告系统中经常遇到的因果推断问题，其中发布者（例如Instagram、TikTok）通过不规则地向每个用户展示通过拍卖选择的广告与人类用户和广告商反复互动。每个处理对应于广告机制的参数值（例如拍卖保留价），我们希望通过实验估计相应的长期处理效应（例如年度广告收入）。在我们的设定中，处理不仅影响展示广告的即时收入，还会改变每个用户的行为轨迹以及每个广告商的出价策略——后者受到有限预算的限制。特别是，每个处理可能甚至会影响人口规模，因为用户会与可容忍的广告机制互动更长时间。我们摒弃了传统的独立同分布假设，将实验测量（例如广告收入）建模为停止随机游走，并使用预算分割实验设计、Anscombe定理、Wald-like方程和中心极限定理来构建长期处理效应的置信区间。
英文摘要：
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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