Open AccessAugmented Inverse Probability Weighting and the Double Robustness Property
Author(s) -
Christoph Kurz
Publication year - 2021
Publication title -
medical decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 103
eISSN - 1552-681X
pISSN - 0272-989X
DOI - 10.1177/0272989x211027181
Subject(s) - inverse probability weighting , estimator , robustness (evolution) , inverse probability , weighting , mathematics , computer science , propensity score matching , inverse , statistics , efficient estimator , regression , minimum variance unbiased estimator , econometrics , mathematical optimization , medicine , bayesian probability , biochemistry , chemistry , geometry , posterior probability , radiology , gene
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]