We analyze the causal effect of online ads on the conversion probability of the users who click on the ad (clickers). We show that designing a randomized experiment to find this effect is infeasible, and propose a method to find the local effect on the clicker conversions. This method is developed in the Potential Outcomes causal model, via Principal Stratification to model non-ignorable post-treatment (or endogenous) variables such as user clicks, and is validated with simulated data. Based on two large-scale randomized experiments, performed for 7.16 million users and 22.7 million users to evaluate ad exposures, a pessimistic analysis for this effect shows a minimum increase of the campaigns effect on the clicker conversion probability of 75% with respect to the non-clickers. This finding contradicts a recent belief that clicks are not indicative of campaign success, and provides guidance in the user targeting task. In addition, we find a larger number of converting users attributed to the overall campaign than those attributed based on the click-to-conversion (C2C) standard business model. This evidence challenges the well-accepted belief that C2C attribution model over-estimates the value of the campaign.
CITATION STYLE
Barajas, J., Akella, R., Flores, A., & Holtan, M. (2015). Estimating ad impact on clicker conversions for causal attribution: A potential outcomes approach. In SIAM International Conference on Data Mining 2015, SDM 2015 (pp. 640–648). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611974010.72
Mendeley helps you to discover research relevant for your work.