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 estimator for quantifying its variability. We apply our method to ad-campaign data from JD.com, which uses such throttling for budget pacing. We show our estimate is statistically different from estimates derived using other standard observational method such as OLS and two-stage least squares estimators based on auction participation as an instrumental variable.
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。变异涉及拍卖节拍,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超过或过分集中在任何一个时期。实施节流机制的方法是根据运动的预算支出率计算参与概率,然后根据这一可能性将运动纳入每个时期可用的随机分类广告。我们显示,使用登录参与概率的方法可以确定在招聘期间的当地平均治疗效果(LATE)。我们提出了一个新的可变的估算,利用这一识别策略,并勾勒出一个从任何一段时间内进行最不集中的估量性观测的路径,以便根据运动的预算支出率来计算参与概率,然后根据这种概率将运动列入每一时期现有的随机分类。我们把我们的方法运用于基于逻辑的逻辑-参与概率,作为基于其他统计方法的统计方法。我们用一种不同的方法来显示我们用不同的计算方法来计算其预算变化。