Papers
1–4 of 4Prediction-Powered Conditional Inference
We study prediction-powered conditional inference in the setting where labeled data are scarce, unlabeled covariates are abundant, and a black-box machine-learning predictor is available. The goal is ...
Stability and Robustness via Regularization: Bandit Inference via Regularized Stochastic Mirror Descent
Statistical inference with bandit data presents fundamental challenges due to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theory. Recent work has ide...
Spatially Robust Inference with Predicted and Missing at Random Labels
When outcome data are expensive or onerous to collect, scientists increasingly substitute predictions from machine learning and AI models for unlabeled cases, a process which has consequences for down...
Statistical Inference via Generative Models: Flow Matching and Causal Inference
Generative AI has achieved remarkable empirical success, but from the perspective of statistics it often remains opaque: its predictions may be accurate, yet the underlying mechanism is difficult to i...