Double robustness without weighting

2019 ◽  
Vol 146 ◽  
pp. 175-180 ◽  
Author(s):  
Myoung-jae Lee ◽  
Sanghyeok Lee
Keyword(s):  
Double Robustness

Model assessment in dynamic treatment regimen estimation via double robustness

Biometrics ◽  
2016 ◽  
Vol 72 (3) ◽  
pp. 855-864 ◽  
Author(s):  
Michael P. Wallace ◽  
Erica E. M. Moodie ◽  
David A. Stephens
Keyword(s):  
Treatment Regimen ◽  
Model Assessment ◽  
Double Robustness ◽  
Dynamic Treatment

scholarly journals A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS

2017 ◽  
Vol 34 (1) ◽  
pp. 112-133 ◽  
Author(s):  
Tymon Słoczyński ◽  
Jeffrey M. Wooldridge
Keyword(s):  
Instrumental Variable ◽  
Exponential Family ◽  
Treatment Effects ◽  
Robust Estimators ◽  
Conditional Mean ◽  
Double Robustness ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Inverse Probability Weighted ◽  
Doubly Robust

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.

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

2017 ◽  
Vol 26 (4) ◽  
pp. 1641-1653 ◽  
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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