A nonparametric doubly robust test for a continuous treatment effect
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Publication:6621539
DOI10.1214/24-aos2405MaRDI QIDQ6621539
Ira Moscovice, Lan Wang, Charles R. Doss, Tongtan Chantarat, Guangwei Weng
Publication date: 18 October 2024
Published in: The Annals of Statistics (Search for Journal in Brave)
Cites Work
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- The wild bootstrap, tamed at last
- Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
- Comment: Performance of double-robust estimators when ``inverse probability weights are highly variable
- Limit theorems for \(U\)-processes
- Nonparametric causal effects based on marginal structural models
- Asymptotic comparison of Cramér-von Mises and nonparametric function estimation techniques for testing goodness-of-fit
- Comparing nonparametric versus parametric regression fits
- Asymptotically minimax hypothesis testing for nonparametric alternatives. I
- Asymptotically minimax hypothesis testing for nonparametric alternatives. II
- Asymptotically minimax hypothesis testing for nonparametric alternatives. III
- Nonparametric analysis of covariance.
- Weak convergence and empirical processes. With applications to statistics
- Goodness-of-fit test for linear models based on local polynomials
- Estimation of partially conditional average treatment effect by double kernel-covariate balancing
- Non-separable models with high-dimensional data
- An Omnibus Non-Parametric Test of Equality in Distribution for Unknown Functions
- Mathematical Foundations of Infinite-Dimensional Statistical Models
- Generalized Additive Models: Some Applications
- Root-N-Consistent Semiparametric Regression
- Uniform Central Limit Theorems
- Asymptotic Statistics
- Probability for Statisticians
- An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model Against a Nonparametric Alternative
- Quasi-oracle estimation of heterogeneous treatment effects
- Debiased machine learning of global and local parameters using regularized Riesz representers
- Debiased machine learning of conditional average treatment effects and other causal functions
- Causal Isotonic Regression
- Double/debiased machine learning for treatment and structural parameters
- Non-parametric Methods for Doubly Robust Estimation of Continuous Treatment Effects
- Uniformly Semiparametric Efficient Estimation of Treatment Effects With a Continuous Treatment
- Generalized Additive and Index Models with Shape Constraints
- Doubly robust nonparametric inference on the average treatment effect
- Super Learner
- Causal Inference With General Treatment Regimes
- Doubly Robust Estimation of Optimal Dosing Strategies
- Nonparametric Tests of the Causal Null With Nondiscrete Exposures
- An asymptotically optimal test for a parametric set of regression functions against a non-parametric alternative
- Towards optimal doubly robust estimation of heterogeneous causal effects
- Orthogonal statistical learning
- Estimating the marginal effect of a continuous exposure on an ordinal outcome using data subject to covariate-driven treatment and visit processes
- Targeted estimation of nuisance parameters to obtain valid statistical inference
- Super-learning of an optimal dynamic treatment rule
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