Improving the causal treatment effect estimation with propensity scores by the bootstrap
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Publication:2081041
DOI10.1007/s10182-021-00427-3OpenAlexW4200134140MaRDI QIDQ2081041
Fulvia Mecatti, Paola Rebora, Maeregu W. Arisido
Publication date: 12 October 2022
Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10182-021-00427-3
simulationcausal inferencepropensity scoreaverage treatment effectobservational studytime-to-event endpointbootstrap bias
Uses Software
Cites Work
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- On the inefficiency of propensity score matching
- Bootstrap methods: another look at the jackknife
- Extreme value statistics for censored data with heavy tails under competing risks
- The jackknife and bootstrap
- A unified principled framework for resampling based on pseudo-populations: asymptotic theory
- On the study of extremes with dependent random right-censoring
- BOOTSTRAP AND k-STEP BOOTSTRAP BIAS CORRECTIONS FOR THE FIXED EFFECTS ESTIMATOR IN NONLINEAR PANEL DATA MODELS
- Computer Age Statistical Inference
- The central role of the propensity score in observational studies for causal effects
- Causal Inference for Statistics, Social, and Biomedical Sciences
- A Bayesian view of doubly robust causal inference: Table 1.
- Functional measure of ozone exposure to model short‐term health effects