Reviews: Distinguishing Distributions When Samples Are Strategically Transformed

The paper introduces a new model of strategic classification. Given some type-dependent feature distribution, agents can transform these features into "signals" according to some background graph. The principal then classifies agents based on the signals. The model is a departure from many recent models of strategic classification, which frame agents as maximizing utility subject to a cost function penalty, and also deals with the setting of repeated samples from agents rather than a one-shot game. This has the benefit of elucidating the importance of differences in the agents' initial feature distribution (in terms of DTV) that may be intuitively true, but has not been captured in recent work.