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Augmented Inverse Probability Weighting and the Double Robustness Property

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  • Christoph F. Kurz

    (Munich School of Management and Munich Center of Health Sciences, Ludwig-Maximilians-Universität Munich, Munich, Germany
    Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg, Germany)

Abstract

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. Highlights • Average treatment effects are often estimated by regression or inverse probability weighting methods, but both are vulnerable to bias. • The augmented inverse probability weighted estimator is an easy-to-use method for average treatment effects that can be less biased because of the double robustness property.

Suggested Citation

  • Christoph F. Kurz, 2022. "Augmented Inverse Probability Weighting and the Double Robustness Property," Medical Decision Making, , vol. 42(2), pages 156-167, February.
    • Handle: RePEc:sae:medema:v:42:y:2022:i:2:p:156-167
    DOI: 10.1177/0272989X211027181
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    References listed on IDEAS

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    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2014. "Mastering ’Metrics: The Path from Cause to Effect," Economics Books, Princeton University Press, edition 1, number 10363.
    2. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    3. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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