題目：Instrument-free Estimation of Endogenous Treatment Effects with an Application to Online Advertising
主講💆🏿♂️：Shakeeb Khan, Professor, Boston College
原定時間：2017年11月2日（周四）16:00-17:30
改為時間👩👦：2017年11月9日（周四）14:00-15:30
Abstract: In this paper we aim to conduct inference on the “lift” effect generated by an online advertisement display: specifically we want to analyze if the presence of the brand ad among the advertisements on the page increases the overall number of consumer clicks on that page. A distinctive feature of online advertising is that the ad displays are highly targeted- the advertising platform evaluates the (unconditional) probability of each consumer clicking on a given ad which leads to a higher probability of displaying the ads that have a higher a priori estimated probability of click. As a result, inferring the causal effect of the ad display on the page clicks by a given consumer from typical observational data is difficult. To address this we use the large scale of our dataset and propose a multi-step an estimator that focuses on the tails of the consumer distribution to estimate the true causal e↵ect of an ad display. This “identification at infinity ” (Chamberlain (1986)) approach alleviates the need for an instrument but results in nonstandard asymptotics, motivating inference methods proposed in Khan and Nekipelov (2016). To validate our estimates, we use a set of large scale randomized controlled experiments that Microsoft has run on its advertising platform. Our non-experimental estimates turn out to be quite close to the results of the randomized controlled trials.
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