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.
翻译:由于实验费用昂贵,观察数据也缺乏随机差异,因此确定数字广告运动在用户一级接触量方面自然发生的、半实验性的差异是一个普遍的来源。它表明广告商如何利用这种差异来查明广告运动的因果关系。这种差异涉及拍卖节拍,一种预算节奏的概率方法,广泛用于在部署期间传播广告预算,这样运动的预算不会在任何一个时期超过或过度集中。这个节流机制是通过根据运动的预算支出率计算参与概率,然后根据这种可能性将运动纳入每一时期可用的随机分类广告活动。我们表明,使用登录参与概率的方法可以确定在招聘期间当地的平均治疗效果(LATE)。我们提出了一个新的估算,利用这一识别策略和描述从任何时期中最不集中的精度程序来量化其变异性。我们用一种我们的方法来计算参与概率的概率,我们用这个方法在现实世界中是使用一种不同的统计方法,我们用一种不同的计算方法来显示我们的标准的汇率。我们用这种方法来用一种不易变的汇率来计算。