Confidence Interval

Confidence intervals quantify the uncertainty associated with estimates derived from data, providing a range of plausible values for a parameter of interest, such as a model's generalization error or a treatment effect. Current research emphasizes robust methods for constructing confidence intervals, particularly in challenging scenarios like few-shot learning, high-dimensional data, and the presence of noise or outliers; techniques like bootstrapping, inverse propensity weighting, and conditional diffusion models are being explored and refined. Accurate confidence interval estimation is crucial for reliable scientific inference and informed decision-making across diverse fields, from machine learning and medical imaging to A/B testing and causal inference.