Auction Throttling and Causal Inference of Online Advertising Effects
广告竞价节流与在线广告效果的因果推断AI 解读 ↓
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广告竞价节流与在线广告效果的因果推断
- 研究问题
- 广告实验成本较高、观测曝光存在选择时，预算节奏控制中的随机竞价参与能否提供识别广告转化效果的准实验变异？
- 方法与思路
- 作者利用竞价节流机制的随机参与和记录的参与概率识别广告活动的 LATE，提出估计量及 bootstrap，并应用于电商广告数据。
- 材料陈述的贡献
- 摘要称，节流日志可用于识别 LATE；应用中的转化提升估计为 110%，区别于朴素观测方法的 600%，合理性仍需全文证据。
- 阅读关注点
- 应核对随机参与如何映射到实际曝光、LATE 的目标人群与识别条件，以及动态参与概率、bootstrap 和比较方法的处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验。摘要未完整说明识别条件，报告数值不能直接推广到其他平台或广告活动。
原文摘要
Causally identifying the effect of digital advertising is challenging, because experimentation is expensive, and observational data lacks random variation. This paper identifies a pervasive source of naturally occurring, quasi-experimental variation in user-level ad-exposure in digital advertising campaigns. It shows how this variation can be utilized by ad-publishers to identify the causal effect of advertising campaigns. The variation pertains to auction throttling, a probabilistic method of budget pacing that is widely used to spread an ad-campaign`s budget over its deployed duration, so that the campaign`s budget is not exceeded or overly concentrated in any one period. The throttling mechanism is implemented by computing a participation probability based on the campaign`s budget spending rate and then including the campaign in a random subset of available ad-auctions each period according to this probability. We show that access to logged-participation probabilities enables identifying the local average treatment effect (LATE) in the ad-campaign. We present a new estimator that leverages this identification strategy and outline a bootstrap procedure for quantifying its variability. We apply our method to real-world ad-campaign data from an e-commerce advertising platform, which uses such throttling for budget pacing. We show our estimate is statistically different from estimates derived using other standard observational methods such as OLS and two-stage least squares estimators. Our estimated conversion lift is 110%, a more plausible number than 600%, the conversion lifts estimated using naive observational methods.