Florian Teschner writes about a recent paper from Bögershausen, Oertzen, & Bock arguing that online ad platforms like Facebook and Google misrepresent the meaning of “A/B testing” for ad campaigns. In A/B testing, we might assume the platform is randomly assigning users to see ad A or ad B, in an attempt to get a clean causal interpretation about which ad is more likely to drive a click (or whatever outcome you’re tracking).
But according to the paper, this is usually not what is happening. Instead, the platform optimizes delivery for each ad independently, steering each one toward the users most likely to click it. In other words, the two ads may be shown to different groups of users, and differences in click-through rates may be attributable to who is seeing the ad, as opposed to the overall appeal of the ad. Ad platforms convert A/B tests from simple randomized experiments into murky observational comparisons. For example, an ad may appear to do better because it happened to be shown disproportionately to a group with a high click-through rate, not because it presents a more compelling overall message. Advertisers get the warm glow of “experimentally backed” marketing without the assurances of randomization.