Causal Inference in the Presence of Interference in Sponsored Search Advertising
This work addresses a domain-specific problem for search engine advertisers and platforms by providing a method to handle interference in ad placements, though it is incremental as it applies existing causal inference frameworks to a new context.
The paper tackles the problem of causal inference in sponsored search advertising where ad placements interfere with each other, violating standard independence assumptions, and demonstrates through experiments on Bing's ad placement system that modeling these interactions improves understanding of user click behavior.
In classical causal inference, inferring cause-effect relations from data relies on the assumption that units are independent and identically distributed. This assumption is violated in settings where units are related through a network of dependencies. An example of such a setting is ad placement in sponsored search advertising, where the clickability of a particular ad is potentially influenced by where it is placed and where other ads are placed on the search result page. In such scenarios, confounding arises due to not only the individual ad-level covariates but also the placements and covariates of other ads in the system. In this paper, we leverage the language of causal inference in the presence of interference to model interactions among the ads. Quantification of such interactions allows us to better understand the click behavior of users, which in turn impacts the revenue of the host search engine and enhances user satisfaction. We illustrate the utility of our formalization through experiments carried out on the ad placement system of the Bing search engine.