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An Introduction to the Augmented Inverse Propensity Weighted Estimator


  • Glynn, Adam N.
  • Quinn, Kevin M.


In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of this estimator is its so-called “double robustness.†Put simply, the estimator remains consistent for the ATE if either the propensity score model or the outcome regression is misspecified but the other is properly specified. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the finite sample performance of the AIPW estimator to three common competitors: a regression estimator, an inverse propensity weighted (IPW) estimator, and a propensity score matching estimator. The Monte Carlo results show that the AIPW estimator has comparable or lower mean square error than the competing estimators when the propensity score and outcome models are both properly specified and, when one of the models is misspecified, the AIPW estimator is superior.

Suggested Citation

  • 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.
  • Handle: RePEc:cup:polals:v:18:y:2010:i:01:p:36-56_01

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    5. Melanie Prague & Rui Wang & Alisa Stephens & Eric Tchetgen Tchetgen & Victor DeGruttola, 2016. "Accounting for interactions and complex inter‐subject dependency in estimating treatment effect in cluster‐randomized trials with missing outcomes," Biometrics, The International Biometric Society, vol. 72(4), pages 1066-1077, December.
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    10. Konan Alain N'ghauran & Corinne Autant-Bernard, 2020. "Assessing the collaboration and network additionality of innovation policies: a counterfactual approach to the French cluster policy," Working Papers halshs-02482546, HAL.
    11. Bailey, Michael & Hopkins, Daniel J. & Rogers, Todd, 2013. "Unresponsive and Unpersuaded: The Unintended Consequences of Voter Persuasion Efforts," Working Paper Series rwp13-034, Harvard University, John F. Kennedy School of Government.
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    14. Moritz Schularick & Ilhyock Shim, 2017. "Household credit in Asia-Pacific," BIS Papers chapters, in: Bank for International Settlements (ed.), Financial systems and the real economy, volume 91, pages 129-144, Bank for International Settlements.
    15. Aloyce R. Kaliba & Anne G. Gongwe & Kizito Mazvimavi & Ashagre Yigletu, 2021. "Impact of Adopting Improved Seeds on Access to Broader Food Groups Among Small-Scale Sorghum Producers in Tanzania," SAGE Open, , vol. 11(1), pages 21582440209, January.
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    20. Mertens, Kewan & Jacobs, Liesbet & Maes, Jan & Kabaseke, Clovis & Maertens, Miet & Poesen, Jean & Kervyn, Matthieu & Vranken, Liesbet, 2015. "The impact of landslides on household income in tropical regions: a case study from the Rwenzori Mountains in Uganda," Working Papers 229008, Katholieke Universiteit Leuven, Centre for Agricultural and Food Economics.
    21. Revilla, Elena & Rodríguez-Prado, Beatriz, 2018. "Bulding ambidexterity through creativity mechanisms: Contextual drivers of innovation success," Research Policy, Elsevier, vol. 47(9), pages 1611-1625.
    22. Long, Wenjin & Pang, Xiaopeng & Dong, Xiao-yuan & Zeng, Junxia, 2020. "Is rented accommodation a good choice for primary school students' academic performance? – Evidence from rural China," China Economic Review, Elsevier, vol. 62(C).

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