Causal Inference in the Presence of Interference in Sponsored Search Advertising

TL;DR

This paper develops a causal inference framework for sponsored search advertising where ad interactions and dependencies violate traditional assumptions, enabling better understanding of user click behavior and revenue impact.

Contribution

It introduces a causal inference model accounting for interference among ads, improving analysis of ad placement effects in search engines.

Findings

Model captures ad interactions and dependencies.

Improves understanding of click behavior.

Enhances revenue and user satisfaction insights.

Abstract

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.