  When users stand to gain from certain predictions, they are prone to act
strategically to obtain favorable predictive outcomes. Whereas most works on
strategic classification consider user actions that manifest as feature
modifications, we study a novel setting in which users decide -- in response to
the learned classifier -- whether to at all participate (or not). For learning
approaches of increasing strategic awareness, we study the effects of
self-selection on learning, and the implications of learning on the composition
of the self-selected population. We then propose a differentiable framework for
learning under self-selective behavior, which can be optimized effectively. We
conclude with experiments on real data and simulated behavior that both
complement our analysis and demonstrate the utility of our approach.
