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Kurz, C.F.

Augmented inverse probability weighting and the double robustness property.

Med. Decis. Making 42, 156-167 (2021)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
This article discusses the augmented inverse propensity weighted (AIPW) estimator as an estimator for average treatment effects. The AIPW combines both the properties of the regression-based estimator and the inverse probability weighted (IPW) estimator and is therefore a "doubly robust" method in that it requires only either the propensity or outcome model to be correctly specified but not both. Even though this estimator has been known for years, it is rarely used in practice. After explaining the estimator and proving the double robustness property, I conduct a simulation study to compare the AIPW efficiency with IPW and regression under different scenarios of misspecification. In 2 real-world examples, I provide a step-by-step guide on implementing the AIPW estimator in practice. I show that it is an easily usable method that extends the IPW to reduce variability and improve estimation accuracy.[Box: see text].
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Double Robustness ; Propensity Score ; Regression ; Simulation Study; Causal Diagrams; Strategies
ISSN (print) / ISBN 0272-989X
e-ISSN 1552-681X
Zeitschrift Medical Decision Making
Quellenangaben Band: 42, Heft: 2, Seiten: 156-167 Artikelnummer: , Supplement: ,
Verlag Sage
Verlagsort 2455 Teller Rd, Thousand Oaks, Ca 91320 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Health Economics and Health Care Management (IGM)