Self-Consistent Conformal Prediction

However, a decision-makers often take identical actions in limitation of CP is that the prediction intervals provide valid contexts with identical predicted outcomes. Conformal coverage only marginally, averaged across all possible contexts prediction helps decision-makers quantify - where'context' refers to the information available for outcome uncertainty for actions, allowing for better decision-making. Thus, for a specific context, CP intervals risk management. Inspired by this perspective, may not accurately capture the true outcome variability, leading we introduce self-consistent conformal prediction, to unreliable and potentially harmful decision-making which yields both Venn-Abers calibrated predictions (van Calster et al., 2019; Lloyd-Jones et al., 2019).