Conditional Mean

Conditional mean estimation focuses on accurately predicting the average value of a target variable given specific input conditions, a crucial task across numerous scientific disciplines. Current research emphasizes developing robust and efficient methods, particularly leveraging deep learning architectures like diffusion models and kernel methods, to improve prediction accuracy and quantify uncertainty in these estimates. This work is driven by the need for reliable statistical inference in complex settings, impacting fields ranging from causal inference and Bayesian analysis to generative modeling and decision-making under uncertainty. The development of theoretically sound and computationally efficient methods for conditional mean estimation continues to be a significant area of active research.