Estimating Ad Impact on Clicker Conversions for Causal Attribution: A Potential Outcomes Approach

Abstract 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.