Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inference
多决策时点下个体处理效应的共形预测区间
Accurately quantifying uncertainty of individual treatment effects (ITEs) across multiple decision points is crucial for personalized decision-making in fields such as healthcare, finance, education, and online marketplaces. Previous work has focused on predicting non-causal longitudinal estimands or constructing prediction bands for ITEs using cross-sectional data based on exchangeability assumptions. We propose a novel method for constructing prediction intervals using…