Double Robustness | ScienceGate

In this paper we study doubly robust estimators of various average and quantile treatment effects under unconfoundedness; we also consider an application to a setting with an instrumental variable. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for subpopulation-specific average treatment effects where subpopulations can be based on covariate values in an arbitrary way. Similar to Wooldridge (2007), we then discuss estimation of the conditional mean using quasi-log likelihoods (QLL) from the linear exponential family.

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Model validation and selection for personalized medicine using dynamic-weighted ordinary least squares

Statistical Methods in Medical Research ◽

10.1177/0962280217708665 ◽

2017 ◽

Vol 26 (4) ◽

pp. 1641-1653 ◽

Cited By ~ 5

Author(s):

Michael P Wallace ◽

Erica EM Moodie ◽

David A Stephens

Keyword(s):

Least Squares ◽

Ordinary Least Squares ◽

Treatment Regime ◽

Model Assessment ◽

Double Robustness ◽

Dynamic Treatment Regime ◽

Dynamic Treatment ◽

Optimal Dynamic ◽

Treatment Alternatives ◽

Least Squares Approach

Model assessment is a standard component of statistical analysis, but it has received relatively little attention within the dynamic treatment regime literature. In this paper, we focus on the dynamic-weighted ordinary least squares approach to optimal dynamic treatment regime estimation, introducing how its double-robustness property may be leveraged for model assessment, and how quasilikelihood may be used for model selection. These ideas are demonstrated through simulation studies, as well as through application to data from the sequenced treatment alternatives to relieve depression study.

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Augmented Inverse Probability Weighting and the Double Robustness Property

Medical Decision Making ◽

10.1177/0272989x211027181 ◽

2021 ◽

pp. 0272989X2110271

Author(s):

Christoph F. Kurz

Keyword(s):

Estimation Accuracy ◽

Probability Weighting ◽

Robust Method ◽

Robustness Property ◽

Inverse Probability ◽

Double Robustness ◽

Average Treatment Effects ◽

Inverse Probability Weighted ◽

Doubly Robust ◽

Augmented Inverse Probability Weighting

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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