Consistent Surrogate

Consistent surrogate models aim to replace computationally expensive or complex objective functions with simpler approximations that maintain crucial properties, such as accurately predicting outcomes under various interventions. Current research focuses on developing and analyzing these surrogates across diverse applications, including black-box optimization, agent-based simulations, and classification problems, employing techniques like polynomial approximations, Bayesian optimization, and causal inference frameworks. This work is significant because accurate and efficient surrogates enable faster experimentation, improved model interpretability, and more robust decision-making in various fields, ranging from engineering optimization to policy analysis.