Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
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Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation. / Gabriel, Erin E.; Sachs, Michael C.; Martinussen, Torben; Waernbaum, Ingeborg; Goetghebeur, Els; Vansteelandt, Stijn; Sjölander, Arvid.
In: Statistics in Medicine, Vol. 43, No. 3, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
AU - Gabriel, Erin E.
AU - Sachs, Michael C.
AU - Martinussen, Torben
AU - Waernbaum, Ingeborg
AU - Goetghebeur, Els
AU - Vansteelandt, Stijn
AU - Sjölander, Arvid
N1 - Publisher Copyright: © 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
PY - 2023
Y1 - 2023
N2 - There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the (Formula presented.) -formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains ‘unbalanced’ even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.
AB - There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the (Formula presented.) -formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains ‘unbalanced’ even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.
KW - causal inference
KW - doubly robust
KW - generalized linear models
U2 - 10.1002/sim.9969
DO - 10.1002/sim.9969
M3 - Journal article
C2 - 38096856
AN - SCOPUS:85179690374
VL - 43
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 3
ER -
ID: 377783421