  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.
