The rise of internet-based services and products in the late 1990s brought about an
unprecedented opportunity for online businesses to engage in large scale data-driven …

A/B testing is critical for modern technological companies to evaluate the effectiveness of
newly developed products against standard baselines. This paper studies optimal designs …

Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are
multiple mutually incompatible definitions of fairness but also because determining what is …

Two-sided marketplaces are standard business models of many online platforms (eg,
Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content …

Users linked together through a network often tend to have similar behaviors. This
phenomenon is usually known as network interaction. Users' characteristics, the covariates …

Online marketplaces use ranking algorithms to determine the rank-ordering of items sold on
their websites. The standard practice is to determine the optimal algorithm using A/B tests …

This tutorial presents state-of-the-art research on causal inference from network data in the
presence of interference. We start by motivating research in this area with real-world …

Online A/B tests have become increasingly popular and important for social platforms.
However, accurately estimating the global average treatment effect (GATE) has proven to be …

A/B test, a simple type of controlled experiment, refers to the statistical procedure of
experimenting to compare two treatments applied to test subjects. For example, many IT …

Social networks are platforms where content creators and consumers share and consume
content. The edge recommendation system, which determines who a member should …