Conformal Predictive

Conformal prediction is a model-agnostic framework for generating prediction intervals with guaranteed coverage probabilities, addressing the critical need for reliable uncertainty quantification in diverse applications. Current research focuses on extending its applicability beyond the standard independent and identically distributed data assumption, particularly to handle covariate shift and continuous treatments, often employing weighted conformal prediction and incorporating techniques like kernel weighting and likelihood ratios. This robust and versatile approach is finding increasing use in various fields, including personalized medicine, electricity market forecasting, and chance-constrained optimization, improving decision-making by providing calibrated uncertainty estimates alongside point predictions.