A GenAI-powered causal inference framework uses deep generative model representations as learned deconfounders to identify dynamic causal effects of video features on real-time outcomes.
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A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.
PPAT residualizes losses via a prediction-powered control variate inside LURE, yielding lower-variance unbiased risk estimates, tailored acquisition, and asymptotic CIs that cover with fewer labels.
Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.
StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
Predicting question-level rectification difficulty from text and allocating human labels by a square-root rule recovers most of the hybrid human–LLM survey efficiency gains without pilot data.
A reference-free proxy scoring framework combined with GIRB calibration produces better-aligned evaluation metrics for summarization and outperforms baselines across seven datasets.
Audits miscalibration in LLM-based social science measurements across 14 constructs and proposes a soft label distillation pipeline that reduces ECE by 43.2% and Brier score by 34.0% on average.
citing papers explorer
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Causal Inference with Video Features as Treatments
A GenAI-powered causal inference framework uses deep generative model representations as learned deconfounders to identify dynamic causal effects of video features on real-time outcomes.
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Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means
A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.
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Prediction-Powered Active Testing
PPAT residualizes losses via a prediction-powered control variate inside LURE, yielding lower-variance unbiased risk estimates, tailored acquisition, and asymptotic CIs that cover with fewer labels.
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Learning U-Statistics with Active Inference
Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.
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Transporting treatment effects by calibrating large-scale observational outcomes
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.
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Stable Localized Conformal Prediction via Transduction
StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
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A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
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Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
Predicting question-level rectification difficulty from text and allocating human labels by a square-root rule recovers most of the hybrid human–LLM survey efficiency gains without pilot data.
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Calibrating Model-Based Evaluation Metrics for Summarization
A reference-free proxy scoring framework combined with GIRB calibration produces better-aligned evaluation metrics for summarization and outperforms baselines across seven datasets.
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Assessing and Mitigating Miscalibration in LLM-Based Social Science Measurement
Audits miscalibration in LLM-based social science measurements across 14 constructs and proposes a soft label distillation pipeline that reduces ECE by 43.2% and Brier score by 34.0% on average.