Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data

DOI10.1093/biomet/asz059zbMath1435.62274arXiv1710.08074OpenAlexW2993749937WikidataQ127198523 ScholiaQ127198523MaRDI QIDQ5222225

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Publication date: 1 April 2020

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1710.08074

zbMATH Keywords

Fisher scoringcausal inferencemodel misspecificationinverse probability weightingpropensity scoreLasso penaltycalibrated estimationregularized M-estimation

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (12)

Recent advances in statistical methodologies in evaluating program for high-dimensional data ⋮ Doubly robust semiparametric inference using regularized calibrated estimation with high-dimensional data ⋮ Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties ⋮ Joint calibrated estimation of inverse probability of treatment and censoring weights for marginal structural models ⋮ Improving Trial Generalizability Using Observational Studies ⋮ Statistical inference using regularized M-estimation in the reproducing kernel Hilbert space for handling missing data ⋮ Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference ⋮ Treatment Effect Estimation Under Additive Hazards Models With High-Dimensional Confounding ⋮ Consistent and robust inference in hazard probability and odds models with discrete-time survival data ⋮ Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data ⋮ Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data ⋮ On doubly robust estimation for logistic partially linear models

Uses Software

• R
• RCAL

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