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In classical causal inference, inferring cause-effect relations from datarelies on the assumption that units are independent and identicallydistributed. This assumption is violated in settings where units are relatedthrough a network of dependencies. An example of such a setting is ad placementin sponsored search advertising, where the clickability of a particular ad ispotentially influenced by where it is placed and where other ads are placed onthe search result page. In such scenarios, confounding arises due to not onlythe individual ad-level covariates but also the placements and covariates ofother ads in the system. In this paper, we leverage the language of causalinference in the presence of interference to model interactions among the ads.Quantification of such interactions allows us to better understand the clickbehavior of users, which in turn impacts the revenue of the host search engineand enhances user satisfaction. We illustrate the utility of our formalizationthrough experiments carried out on the ad placement system of the Bing searchengine.

Causal Inference in the Presence of Interference in Sponsored Search Advertising