Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation
数据边界上的推断：用高斯过程处理模型依赖、重叠不足与外推
Many inferential tasks involve fitting models to observed data and predicting outcomes at new covariate values, requiring interpolation or extrapolation. Conventional methods select a single best-fitting model, discarding fits that were similarly plausible in-sample but would yield sharply different predictions out-of-sample. Gaussian Processes (GPs) offer a principled alternative. Rather than committing to one conditional expectation function, GPs deliver a posterior…