IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v42y2022i2p156-167.html
   My bibliography  Save this article

Augmented Inverse Probability Weighting and the Double Robustness Property

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X211027181
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X211027181?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khudri, Md Mohsan & Hussey, Andrew, 2024. "Breastfeeding and Child Development Outcomes across Early Childhood and Adolescence: Doubly Robust Estimation with Machine Learning," IZA Discussion Papers 17080, Institute of Labor Economics (IZA).
    2. Yang Yang, 2023. "Hukou Identity and Economic Behaviours: A Social Identity Perspective," Erudite Ph.D Dissertations, Erudite, number ph23-02 edited by Catherine Bros & Julie Lochard.
    3. Hongyun Zheng & Wanglin Ma & Junpeng Li & Julio Botero, 2023. "Relationship between Internet Use and Negative Affect," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 18(4), pages 1693-1713, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heigle, Julia & Pfeiffer, Friedhelm, 2019. "An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 222378.
    2. Toro, Weily & Tigre, Robson & Sampaio, Breno, 2015. "Daylight Saving Time and incidence of myocardial infarction: Evidence from a regression discontinuity design," Economics Letters, Elsevier, vol. 136(C), pages 1-4.
    3. Caloffi, Annalisa & Freo, Marzia & Ghinoi, Stefano & Mariani, Marco & Rossi, Federica, 2022. "Assessing the effects of a deliberate policy mix: The case of technology and innovation advisory services and innovation vouchers," Research Policy, Elsevier, vol. 51(6).
    4. Florian Gunsilius & Yuliang Xu, 2021. "Matching for causal effects via multimarginal unbalanced optimal transport," Papers 2112.04398, arXiv.org, revised Jul 2022.
    5. Özler, Berk & Çelik, Çiğdem & Cunningham, Scott & Cuevas, P. Facundo & Parisotto, Luca, 2021. "Children on the move: Progressive redistribution of humanitarian cash transfers among refugees," Journal of Development Economics, Elsevier, vol. 153(C).
    6. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    7. Andrea Mercatanti & Fan Li, 2017. "Do debit cards decrease cash demand?: causal inference and sensitivity analysis using principal stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 759-776, August.
    8. Sun, Shanxia & Delgado, Michael & Khanna, Neha, 2017. "Hybrid Vehicles and Household Driving Behavior: Implications for Miles Traveled and Gasoline Consumption," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258502, Agricultural and Applied Economics Association.
    9. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    10. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    11. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    12. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    13. Dominik M Piehlmaier & J Jeffrey Inman & Andrew T Stephen & Andrew T Stephen, 2023. "The One-Man Show: The Effect of Joint Decision-Making on Investor Overconfidence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 50(2), pages 426-446.
    14. Raffaele Guetto & Valentina Tocchioni & Daniele Vignoli, 2023. "The Causal Impact of Temporary Employment on First Births in Italy: An Update," Econometrics Working Papers Archive 2023_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    15. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    16. Lars Isenhardt & Stefan Seifert & Silke Hüttel, 2023. "Tenant Favoritism and Right of First Refusals in Farmland Auctions: Competition and Price Effects," Land Economics, University of Wisconsin Press, vol. 99(2), pages 302-324.
    17. Bernard Koch & Tim Sainburg & Pablo Geraldo & Song Jiang & Yizhou Sun & Jacob Gates Foster, 2021. "A Primer on Deep Learning for Causal Inference," Papers 2110.04442, arXiv.org, revised Nov 2023.
    18. Pauline Leung & Zhuan Pei,, 2020. "Further Education During Unemployment," Working Papers 642, Princeton University, Department of Economics, Industrial Relations Section..
    19. Tenglong Li & Kenneth A. Frank & Mingming Chen, 2024. "A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study," Mathematics, MDPI, vol. 12(3), pages 1-14, January.
    20. Tamara Bischof & Boris Kaiser, 2021. "Who cares when you close down? The effects of primary care practice closures on patients," Health Economics, John Wiley & Sons, Ltd., vol. 30(9), pages 2004-2025, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:42:y:2022:i:2:p:156-167. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.