Proxy Metric

Proxy metrics are surrogate measures used in various fields when direct measurement of a target variable is difficult or expensive. Current research focuses on developing efficient algorithms for designing and selecting optimal proxy metrics, particularly within causal inference, deep metric learning, and recommender systems, often employing techniques like domain adaptation, portfolio optimization, and kernel methods. These advancements improve the accuracy and efficiency of analyses across diverse applications, ranging from causal effect estimation and 3D asset generation to improving the reliability of predictive models and optimizing recommendation systems. The ultimate goal is to enhance the reliability and interpretability of scientific findings and improve decision-making in practical settings.