Past Meetup

Estimating The Causal Effect Of Online Display Advertising

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141 people went


We are excited to have Ori Stitelman of Media6Degrees join us in August to discuss his important work on estimating causal effects of display advertising. Ori's abstract & bio follows...

ABSTRACT- This talk will examine ways to estimate the causal effect of display advertising on browser post-view conversion (i.e. visiting the site after viewing the ad rather than clicking on the ad to get to the site). The effectiveness of online display ads beyond simple click-through evaluation is not well established in the literature. Are the high conversion rates seen for subsets of browsers the result of choosing to display ads to a group that has a naturally higher tendency to convert, or does the advertisement itself cause an additional lift? How does showing an ad to different segments of the population affect their tendencies to take a specific action, or convert? We present an approach for assessing the effect of display advertising on customer conversion that does not require the cumbersome and expensive setup of a controlled experiment, but rather uses the observed events in a regular campaign setting. Our general approach can be applied to many additional types of causal questions in display advertising.

BIO-Prior to joining Media6Degrees, Ori received a Ph.D. in Biostatistics from the University of California, Berkeley. His research concentrated on developing methods for estimating causal effects. In particular, he focused on estimating causal effects in observational, censored, and longitudinal data. At UC Berkeley, Ori and his advisor, Dr. Mark van der Laan, focused on Targeted Maximum Likelihood Estimation (TMLE), a class of doubly robust semi-parametric estimators that focus on estimating a causal parameter that directly answers a business or scientific question of interest. In addition to publishing several papers on the topic, Ori recently contributed two chapters to the first book published on TMLE, "Targeted Learning: Causal Inference for Observational and Experimental Data" by Mark van der Laan and Sherri Rose. To date, TMLE has been primarily applied to health outcomes data. However, the causal methods he and his group developed while at UC Berkeley are widely applicable across many different subject areas. Currently, Ori is working with his colleagues at Media6Degrees to implement those same methods to estimate the causal effect of display advertising on browser conversion.


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