Regression Function

Regression function estimation aims to model the relationship between a dependent variable and one or more independent variables, a fundamental problem across numerous scientific disciplines. Current research emphasizes improving the accuracy and efficiency of regression function estimation, particularly in high-dimensional settings, using advanced techniques like tensor neural networks, deep learning architectures, and kernel methods. These advancements are crucial for addressing challenges in causal inference (e.g., estimating conditional average treatment effects), handling complex data structures (e.g., spatial data, distributions of distributions), and ensuring fairness in predictive modeling. The resulting improvements in regression accuracy and interpretability have significant implications for various fields, including economics, medicine, and scientific modeling.