Paper develops minimax rates for non-exact sparse models in high dimensions.
problem Estimating non-exact sparse models in high-dimensional data.
method Formalizes approximate sparsity, derives minimax rates, proposes an estimator.
result Achieves minimax optimal rates for non-exact sparse models.
Improved nonparametric regression with debiasing for root-n consistency.
problem Challenges in achieving root-n consistency and normal distribution for nonparametric estimators.
method Debiasing technique by adding a correction term to nonparametric estimators.
result Achieves root-n consistency and asymptotic normality.
Improved inference of mean outcome with less data.
problem Improving inference of mean outcome with limited labeled data.
method k-fold, cross-fitted, double robust estimator for root-n inference.
result Root-n inference of mean outcome possible with less labeled data.
Unified framework for bandit convex optimization with noisy gradient oracles.
problem Optimizing functions with noisy gradient feedback.
method Abstract oracle for gradient estimation, unifying previous methods.
result Achieving optimal root-n rate requires new algorithms or proof techniques.
Paper develops a new estimator for dynamic treatment effects in high-dimensional settings.
problem Time-varying confounding and model misspecification in estimating dynamic treatment effects.
method Sequential model doubly robust estimator with moment-targeting estimates.
result Root-N inference achieved under model misspecification, even with high-dimensional covariates.
New Riemannian radial distributions help estimate parameters on symmetric spaces.
problem Challenges in manifold data analysis due to lack of parametric distributions.
method Introduced Riemannian radial distributions on symmetric spaces, utilized symmetry, and developed M-estimators.
result MLE achieves root-n convergence rate up to logarithmic terms, demonstrating optimality.
Deep neural networks improve mean function estimation for functional data.
problem Estimating mean functions of functional data.
method Deep neural networks with ReLU activation, sparsely connected.
result Achieves optimal nonparametric convergence rate in empirical norm.
The paper offers methods to estimate and infer the boundary of a set-identified linear model.
problem Estimating and inferring the boundary of a set-identified linear model with many covariates.
method The paper uses semiparametric moment equations and Neyman-orthogonality combined with sample splitting to construct a root-N consistent, uniformly asymptotically Gaussian estimator and a multiplier bootstrap procedure for inference.
result The paper provides a method to estimate and infer the boundary of a set-identified linear model.
Study singularity structures in finite mixtures affecting parameter estimation rates.
problem Understanding how singularity structures impact parameter estimation in finite mixtures.
method Developed a general framework to identify singularity structures in finite mixtures and studied their effects on convergence rates and minimax lower bounds.
result Established convergence rates for finite mixtures of skew-normal distributions, revealing complex asymptotic behaviors.
Parameters defined via General Estimating Equations (GEE) can be estimated by maximizing the Empirical Likelihood (EL). Newey and Smith (2004) have recently shown that this EL estimator exhibits desirable higher-order asymptotic properties, namely, that its O(n^-1) bias is small and that bias-corrected EL is higher-ord…
Parameters defined via general estimating equations (GEE) can be estimated by maximizing the empirical likelihood (EL). Newey and Smith [Econometrica 72 (2004) 219--255] have recently shown that this EL estimator exhibits desirable higher-order asymptotic properties, namely, that its O ( n − 1 ) O(n^{-1}) O ( n − 1 ) bias is small and that …
We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in the large-sample limit. The asymptotic distributions are derived for both the case where the estimators are tuned to perform consistent model selection and for the case where the estimators are tuned to perform conserva…
New method implicitly regularizes high-dimensional linear regression using gradient descent.
problem Sparse vector estimation in high-dimensional linear regression.
method Gradient descent on residual sum of squares with early stopping under overparameterization.
result Gradient descent implicitly leads to nearly sparse optimal solutions without explicit penalties.
New strategy debiases synthetic data generated by DGMs for improved statistical inference.
problem Bias and imprecision in synthetic data generated by DGMs impede statistical convergence and inference.
method Debiasing strategy based on debiased and targeted machine learning.
result Enhanced convergence rates and accurate estimators with easily approximated variances.
Unified approach for estimating causal contrasts robust to model misspecification.
problem Estimating causal contrasts in non-parametric models with potential misspecification.
method A unifying approach using doubly-robust ℓ 1 \ell_1 ℓ 1 regularized estimation with cross-fitting and sample splitting. result Estimators are robust to misspecification of either nuisance function, ensuring consistent and normal asymptotic properties.
Paper proposes CIV estimator for categorical instruments in small sample settings.
problem Estimation with categorical instruments in settings with few observations per category.
method CIV estimator leveraging regularization assumption for latent categorical variable.
result CIV estimator is asymptotically normal, efficient, and semiparametrically efficient under homoskedasticity.
Kernel ridge regression for causal inference with missing data.
problem Estimating treatment effects with missing data in selected samples.
method Kernel ridge regression estimators for nonparametric dose response curves and semiparametric treatment effects.
result Uniform consistency and finite sample rates for continuous treatment, root-n consistency for discrete treatment.
Proposes a new estimator for causal mediation with continuous treatments.
problem Estimation of direct and indirect effects with continuous treatments.
method Kernel smoothing approach with cross-fitting for non-parametric estimation.
result Multiply robust and asymptotically normal estimator for continuous treatments.
Paper develops PGMM framework for debiased inference on nonparametric IV estimators.
problem Automatic debiased inference on nonparametric IV functionals.
method Penalized GMM (PGMM) framework for functionals of IV estimators.
result PGMM-based debiased estimator performs well, achieving near-nominal coverage.
New method estimates optimal dose intervals for personalized treatment.
problem Learning optimal dose intervals from observational data.
method Probability dose interval (PDI) method using DC algorithm.
result Consistent policy with risk converging to best-in-class at root-n rate.
Proposes Causal k-Means Clustering to identify subgroup effects.
problem Identifying subgroup effects with heterogeneous treatment effects.
method Leverages k-means clustering to uncover unknown subgroup structure.
result Developed bias-corrected estimator with fast root-n rates and asymptotic normality.
New method calibrates asynchronous, error-prone covariates for longitudinal data.
problem Estimation biases and slow convergence in analyzing time-varying covariates with measurement error.
method Functional calibration approach based on functional principal component analysis.
result Asymptotically unbiased and consistent estimators for time-invariant coefficients; optimal convergence rate for time-varying coefficients.
New matching estimators correct bias in multivariate settings without smoothing parameters.
problem Bias in nearest-neighbor and matching estimators in multiple dimensions.
method Polynomial least squares fits on Voronoi tessellations.
result Novel estimators converge at n \sqrt{n} n rate under mild smoothness assumptions. Proposes debiasing strategy for ill-posed regression problems.
problem Estimating functions with conditional moment restrictions, especially when estimators are sensitive to misspecification.
method Debiased estimation using influence function of modified mean squared error.
result Demonstrates finite-sample convergence rate and robustness to misspecification.
This paper tackles robust policy learning under concept drifts, improving upon existing methods.
problem Tackles robust policy learning under concept drifts, improving upon existing methods.
method Develops a doubly-robust estimator and a learning algorithm to maximize policy value within a given policy class.
result The proposed algorithm achieves sub-optimality gap of the order κ ( Π ) n − 1 / 2 κ(Π)n^{-1/2} κ ( Π ) n − 1/2 , demonstrating substantial improvement over existing benchmarks. Proposes a robust method to estimate causal effects with high-dimensional covariates.
problem Estimating causal effects in studies with many confounders.
method Penalized M-estimators for propensity score and outcome models, followed by covariate balancing.
result Root-n consistent, asymptotically normal estimator with semiparametric efficiency.
Estimates sub-Gaussian parameter with consistent and optimal rates.
problem Estimating sub-Gaussian parameter from random variables.
method Constrained maximization of empirical weighted cumulant generating function.
result Root-n rate estimator is consistent and optimal under certain conditions.
Kernel ridge regression imputation with consistent variance estimation for handling missing data.
problem Handling missing data in statistical analysis.
method Kernel ridge regression imputation combined with entropy method for variance estimation.
result Root-n consistency of the imputation estimator in a Sobolev space setting.
Efficient estimators for smooth Hilbert-valued parameters with theoretical guarantees.
problem Estimating smooth Hilbert-valued parameters with theoretical guarantees.
method Pathwise differentiable Hilbert-valued parameters, efficient influence functions, regularized one-step estimators.
result Theoretical guarantees for efficient estimators even when nuisance functions are arbitrary.
New estimators for causal effects in DAGs with hidden variables, addressing computational and statistical challenges.
problem Estimating causal effects in DAGs with hidden variables beyond traditional criteria.
method Introduces novel one-step corrected plug-in and targeted minimum loss-based estimators for causal effects in DAGs with hidden variables.
result Root-n consistent causal effect estimates with desirable statistical properties.
Improves ML methods for causal parameter estimation.
problem Poor performance of ML methods in estimating causal parameters.
method Double/Debiased Machine Learning (double ML) method.
result Estimates of causal parameters can be made approximately unbiased and normal.
New method resolves causal heterogeneity by defining a resolution profile.
problem Causal subgroup analyses often oversimplify heterogeneity into a small number of groups.
method Introduces a resolution profile as a functional of the causal feature law, using Bayesian-bootstrap inference.
result Shows that the resolution profile is a continuous path with discontinuities at knots, providing integer-valued subgroup numbers.
The study assesses external validity by evaluating worst-case treatment effects across subpopulations.
problem Underrepresentation of marginalized groups and limited study populations.
method Develops a semiparametrically efficient estimator for worst-case treatment effects (WTE) and uses cross-fitting to guard against brittle findings.
result The proposed framework guards against invalid findings due to unanticipated population shifts.
New estimator handles covariate shift with closed-form solution and super-efficiency.
problem Handling covariate shift in missing data and causal inference problems.
method Minimum Wasserstein distance estimation framework.
result Closed-form expression and super-efficiency relative to semiparametric efficient estimator.
The paper uses neural networks to estimate treatment effects even with many confounders.
problem Estimating treatment effects with a growing number of confounders.
method General optimization framework using neural networks to approximate nuisance functions.
result Neural networks can handle a diverging number of confounders and alleviate the curse of dimensionality.
Improved quantile estimation using semi-supervised data.
problem Quantile estimation in high-dimensional settings with limited labeled data.
method Proposes semi-supervised estimators using a flexible imputation strategy and debiasing step.
result Improved estimation accuracy compared to supervised methods, robust to misspecification.
New approach for distributed learning of Gaussian mixtures.
problem Large datasets distributed across different centers.
method Split-and-conquer approach with MM algorithm.
result New estimator is consistent and retains root-n consistency.
Develops robust and efficient SS estimators for treatment effects.
problem Estimating treatment effects in semi-supervised settings with limited labeled data.
method A family of SS estimators using labeled and unlabeled data, ensuring robustness and efficiency.
result Root-n consistency and asymptotic normality of SS estimators under correct specification of propensity score and nuisance functions.
Quantum method speeds up risk estimation for insurance tail risks.
problem Sample-sparsity in classical Monte Carlo methods for tail risk pricing.
method Quantum Amplitude Estimation (QAE) with Grover amplification.
result Quantum method achieves convergence approaching order reciprocal N, enabling high-resolution tail estimation within practical budgets.
This paper explains the theoretical regularization effect of FGSM and its generalization.
problem Understanding the theoretical properties of FGSM and its generalization.
method Developed asymptotic properties of Generalized FGSM in a simple neural network setting.
result Generalized FGSM estimation is root n-consistent and weakly oracle under proper conditions.
Proposes a new method for analyzing multimodal neuroimaging data.
problem Combining interpretability and flexibility in multimodal data analysis.
method Orthogonalized kernel debiased machine learning approach.
result Established consistency and asymptotic normality of the estimated primary parameter.
Estimates long-term effects of new treatments using historical and short-term data.
problem Estimating long-term effects of novel treatments with limited historical data.
method Surrogate indices, dynamic treatment effect estimation, and double machine learning combined in a unified pipeline.
result Consistent and asymptotically normal estimates of long-term effects under Markovian assumption.
The paper develops methods to handle missing data using regularized M-estimation in reproducing kernel Hilbert space.
problem Handling missing data in statistical analysis.
method Kernel ridge regression for imputation and maximum entropy method for propensity score estimation.
result The proposed methods achieve statistical consistency and asymptotic equivalence.
Proposes a novel network-based neighborhood regression for biological systems.
problem Lack of comprehensive analysis on biological modules using both global and local network data.
method Develops a community-wise least square optimization approach to analyze gene modules and their regulatory strength.
result Achieves exact minimax optimality and linear consistency in identifying gene module associations.
Unified framework improves option pricing accuracy and stability.
problem Combining structured knowledge with data for better financial modeling.
method Structured-Knowledge-Informed Neural Networks (SKINNs) that embed theoretical insights into neural networks.
result SKINNs improve out-of-sample valuation and hedging performance in financial applications.
Tests whether a treatment's effect is fully mediated by observed outcomes and identifies causal mechanisms.
problem Understanding how a treatment affects an outcome through intermediate variables.
method Proposes a test to evaluate full mediation and causal mechanism identification, extending to non-randomly assigned treatments.
result A conditionally random treatment is conditionally independent of the outcome given mediators and covariates if full mediation and causal mechanism identification hold.
New methods estimate causal effects using front-door criterion in presence of unmeasured confounders.
problem Estimating causal effects in observational studies with unmeasured confounders.
method Developed novel one-step and targeted minimum loss-based estimators for front-door assumptions.
result Established conditions for root-n consistency and asymptotic linearity.
Develops NPMC method for noisy labels, improving multiclass classification accuracy.
problem Asymmetric misclassification costs and label noise in multiclass classification.
method Empirical likelihood approach using exponential tilting density ratio model.
result Root n consistent and asymptotically normal estimators for clean labels and noise mechanism.