Conformal Prediction Interval

Conformal prediction intervals offer a distribution-free method for quantifying uncertainty in predictions, guaranteeing a specified coverage probability without strong assumptions about the data's underlying distribution. Recent research focuses on extending conformal prediction to diverse settings, including time series data, causal inference with continuous treatments, and parameter estimation, often employing techniques like quantile regression, kernel methods, and adaptive calibration strategies to improve interval sharpness and efficiency. This robust approach has significant implications for various fields, enabling reliable uncertainty quantification in applications ranging from healthcare and personalized medicine to autonomous systems and industrial process monitoring.