arXiv AI
Jun 4
Generative Augmented Inference
arXiv:2604. 14575v2 Announce Type: replace-cross Abstract: Large language models enable inexpensive AI-generated annotations, but using them reliably for causal inference remains challenging.
Generative Augmented Inference of LLM-generated Data for Market Research: Theory and Empirical Evidence
arXiv:2604. 14575v3 Announce Type: replace-cross Abstract: Marketing research often relies on parameters estimated from costly human-generated data, such as conjoint survey responses, purchase decisions, and field experiment outcomes.
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arXiv Machine Learning
Jul 21
BACON: Budgeted Human Calibration for Modeling and Evaluation with Multiple AI Judges
arXiv:2607. 16239v1 Announce Type: new Abstract: AI judges offer a scalable, low-cost alternative to human evaluation, but their outputs can be biased relative to human preferences and highly item-dependent, varying across judges, tasks, and domains.
arXiv AI
Jul 13
Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
arXiv:2604. 17267v2 Announce Type: replace Abstract: Large Language Models can generate synthetic survey responses at low cost, but their accuracy varies unpredictably across questions.
arXiv Machine Learning
Aug 11
Demystifying Prediction Powered Inference
arXiv:2601. 20819v2 Announce Type: replace-cross Abstract: Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science.
arXiv Machine Learning
Jun 26
No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference
arXiv:2505. 20178v2 Announce Type: replace-cross Abstract: Prediction-Powered Inference (PPI) is a popular strategy for combining gold-standard and possibly noisy pseudo-labels to perform statistical estimation.
arXiv AI
Jun 30
HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
arXiv:2606. 29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity.
arXiv Machine Learning
Jun 26
Learning from a Biased Sample
arXiv:2209. 01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed.
arXiv AI
Jun 30
Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data
arXiv:2606. 30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling bias.
arXiv Machine Learning
Jun 9
Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
arXiv:2602. 16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard estimators are biased and the estimand is not identified without additional assumptions.
arXiv Machine Learning
5d ago
Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization
arXiv:2608. 13461v1 Announce Type: new Abstract: Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the conversion process.
arXiv Machine Learning
Jun 8
MIST: Mutual Information Estimation Via Supervised Training
arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.
arXiv Machine Learning
Jul 16
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
arXiv:2511. 01680v4 Announce Type: replace-cross Abstract: Social scientists are increasingly turning to unstructured datasets to unlock new empirical insights, e.