We propose a framework based on evolutionary game theory to model feedback loops in supervised learning, and use it to study how different learning settings affect long-term outcomes.

We propose DCCD-CONF, a scalable, theoretically grounded framework for learning nonlinear cyclic causal graphs with unmeasured confounders using interventional data.