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Learning a Size-Weight Frontier for Synthetic-Augmented Inference
Chengpiao Huang, Kaizheng Wang
Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and lead to unreliable inference.…
Tuning-Free Covariance-Aware Sequential Shrinkage for Multi-Source Estimation
Wenbo Jing, Xi Chen, Yaqi Duan +2
Modern statistical learning problems often involve multiple related data sets, in which learning efficiency on a target set can be improved by borrowing information from related so…
Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression
Seok-Jin Kim, Kaizheng Wang
We study the problem of estimating the effect function for a continuous treatment, which maps each treatment value to a population-averaged outcome. A central challenge in this set…
How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
Chengpiao Huang, Yuhang Wu, Kaizheng Wang
Large language models (LLMs) are increasingly used to simulate survey responses, but synthetic data can be misaligned with the human population, leading to unreliable inference. We…
Distribution-Free Predictive Inference under Unknown Temporal Drift
Elise Han, Chengpiao Huang, Kaizheng Wang
Distribution-free prediction sets play a pivotal role in uncertainty quantification for complex statistical models. Their validity hinges on reliable calibration data, which may no…