A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
Handbook of econometrics , volume=
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Proposes a novel semi-supervised estimator for risk prediction under double censoring that combines limited gold-standard labels with large-scale surrogates, proves theoretical validity, and shows efficiency gains over supervised methods in simulations and a T2D EHR application.
A Neyman-orthogonal estimator paired with Lasso nuisance estimation achieves root-T asymptotic normality for BLP demand parameters under high-dimensional controls and approximate sparsity.
A method using predicted rectification difficulty for optimal human sample allocation in LLM-augmented surveys captures 61-79% of theoretical efficiency gains and reduces MSE by 11% on two datasets without pilot data.
A learnable continuous perturbation framework for LLM token prefixes via latent vector transformations, optimized through unbiased estimating equations, yields gains in out-of-domain performance.
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
Develops asymptotic theory and bootstrap inference for the τ-quantile of cross-sectional individual coefficient distributions in panel data under stochastic and deterministic designs.
A model-free estimator for causal effects in two-sample Mendelian randomization that is consistent and asymptotically normal under population heterogeneity between samples.
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Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials
A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
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Semi-supervised Method for Risk Prediction with Doubly Censored EHR Data
Proposes a novel semi-supervised estimator for risk prediction under double censoring that combines limited gold-standard labels with large-scale surrogates, proves theoretical validity, and shows efficiency gains over supervised methods in simulations and a T2D EHR application.
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Estimation of BLP models with high-dimensional controls
A Neyman-orthogonal estimator paired with Lasso nuisance estimation achieves root-T asymptotic normality for BLP demand parameters under high-dimensional controls and approximate sparsity.
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Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
A method using predicted rectification difficulty for optimal human sample allocation in LLM-augmented surveys captures 61-79% of theoretical efficiency gains and reduces MSE by 11% on two datasets without pilot data.
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Learning Perturbations to Extrapolate Your LLM
A learnable continuous perturbation framework for LLM token prefixes via latent vector transformations, optimized through unbiased estimating equations, yields gains in out-of-domain performance.
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An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
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Estimation and Inference for the $\tau$-Quantile of Individual Heterogeneous Coefficient
Develops asymptotic theory and bootstrap inference for the τ-quantile of cross-sectional individual coefficient distributions in panel data under stochastic and deterministic designs.
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A Robust Framework for Two-Sample Mendelian Randomization under Population Heterogeneity
A model-free estimator for causal effects in two-sample Mendelian randomization that is consistent and asymptotically normal under population heterogeneity between samples.