Proposes task exchangeability as a condition for valid inference when using synthetic data in scientific research, with methods and extensions demonstrated on surveys and AI evaluations.
Prediction-powered inference with imputed covariates and nonuniform sampling.arXiv preprint arXiv:2501.18577
11 Pith papers cite this work. Polarity classification is still indexing.
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dShrink is a model-free transfer estimator using summary statistics that is guaranteed to have lower expected quadratic error than the target-only estimator under arbitrary population heterogeneity.
Introduces convolution smoothing of the check-loss for prediction-powered quantile regression, derives asymptotics under misspecification, and proposes an ensemble estimator.
OPAL learns optimal smooth labeling policies from ML uncertainty scores to enable low-variance prediction-assisted inference with finite-sample coverage guarantees.
Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.
Empirical Bayes rebiasing learns the bias distribution from paired noisy estimates to produce shorter calibrated intervals than full debiasing while maintaining coverage.
A doubly robust, asymptotically normal estimator for regression with completely missing covariates across populations, combining importance weighting and moment imputation under a sub-population shift assumption.
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.
Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.
Introduces D2S3 semiparametric framework that extends AIPW estimators to semi-supervised settings with MAR labeling, distribution shift, and decaying overlap, supplying corrected asymptotic rates instead of root-n convergence.
GLIDE is a Python library that packages multiple PPI estimators and samplers for reliable GenAI evaluation and reports annotation savings in an agentic case study.
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