Doubly robust semiparametric inference using regularized calibrated estimation with high-dimensional data
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Publication:2137036
DOI10.3150/21-BEJ1378OpenAlexW3088487772MaRDI QIDQ2137036
Publication date: 16 May 2022
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2009.12033
semiparametric estimationhigh-dimensional datadouble robustnesspartially linear modelaverage treatment effectcalibration estimationLasso penaltydebiased Lasso
Cites Work
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- A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
- Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
- High-dimensional inference in misspecified linear models
- Robust inference on average treatment effects with possibly more covariates than observations
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models
- Statistics for high-dimensional data. Methods, theory and applications.
- Improved variable selection with forward-lasso adaptive shrinkage
- Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness and sensitivity analysis
- A unified theory of confidence regions and testing for high-dimensional estimating equations
- On doubly robust estimation for logistic partially linear models
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Bounded, efficient and doubly robust estimation with inverse weighting
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- Doubly robust instrumental variable regression
- A Distributional Approach for Causal Inference Using Propensity Scores
- Inference and missing data
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Asymptotic Statistics
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
- A Semiparametric Approach to Dimension Reduction
- Inference for treatment effect parameters in potentially misspecified high-dimensional models
- Double/debiased machine learning for treatment and structural parameters
- Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
- Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data
- On doubly robust estimation in a semiparametric odds ratio model
- Bias-Reduced Doubly Robust Estimation
- Doubly robust inference with missing data in survey sampling
- A Semiparametric Odds Ratio Model for Measuring Association
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- Maximum Likelihood Estimation of Misspecified Models
- A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
- Doubly robust tests of exposure effects under high‐dimensional confounding
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