Qini curve estimation under clustered network interference

Qini curves are a widely used tool for assessing treatment policies under allocation constraints as they visualize the incremental gain of a new treatment policy versus the cost of its implementation. Standard Qini curve estimation assumes no interference between units: that is, that treating one unit does not influence the outcome of any other unit. In many real-life applications such as public policy or marketing, however, the presence of interference is common. Ignoring interference in these scenarios can lead to systematically biased Qini curves that over- or under-estimate a treatment policy's cost-effectiveness. In this paper, we address the problem of Qini curve estimation under clustered network interference, where interfering units form independent clusters. We propose a formal description of the problem setting with an experimental study design under which we can account for clustered network interference. Within this framework, we introduce three different estimation strategies suited for different conditions. Moreover, we introduce a marketplace simulator that emulates clustered network interference in a typical e-commerce setting. From both theoretical and empirical insights, we provide recommendations in choosing the best estimation strategy by identifying an inherent bias-variance trade-off among the estimation strategies.