Ensemble and calibration multiply robust estimation for quantile treatment effect
From MaRDI portal
Publication:5044694
DOI10.1080/02664763.2021.1966397OpenAlexW3195893165MaRDI QIDQ5044694
Publication date: 2 November 2022
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2021.1966397
calibrationcausal inferencepropensity scorequantile treatment effectensemble approachmultiply robust
Uses Software
Cites Work
- Doubly Robust Estimation in Missing Data and Causal Inference Models
- Efficient Semiparametric Estimation of Quantile Treatment Effects
- Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation
- Oracle, multiple robust and multipurpose calibration in a missing response problem
- Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
- Parameter estimation through semiparametric quantile regression imputation
- Empirical likelihood and general estimating equations
- Empirical probability plots and statistical inference for nonlinear models in the two-sample case
- Semiparametric instrumental variable estimation of treatment response models.
- Efficient semiparametric estimation of multi-valued treatment effects under ignorability
- Multiple Imputation After 18+ Years
- An efficient empirical likelihood approach for estimating equations with missing data
- Identification and Estimation of Local Average Treatment Effects
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys
- Ensemble Approaches to Estimating the Population Mean with Missing Response
- Multiply Robust Estimation in Regression Analysis With Missing Data
- A General Framework for Quantile Estimation with Incomplete Data
- Efficient Quantile Regression Analysis With Missing Observations
- Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out: Table 1.
- OUP accepted manuscript
- An IV Model of Quantile Treatment Effects
- Estimation with missing data: beyond double robustness
- Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings
- A Generalization of Sampling Without Replacement From a Finite Universe
This page was built for publication: Ensemble and calibration multiply robust estimation for quantile treatment effect