An Introduction to the Augmented Inverse Propensity Weighted Estimator

By Adam Glynn, Kevin M. Quinn. Political Analysis, 2010

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The performance of estimators based on the propensity score (2013, background): “31 Robins, Rotnitzky and Zhao (1994) and Robins and Rotnitzky (1995) show that DR is semi-parametrically efficient if both model components are correctly specified (see also the discussions in Robins, Mark and Newey, 1992, Scharfstein, Rotnitzky and Robins, 1999, Hirano and Imbens, 2001, Lunceford a”
The causal interpretation of estimated associations in regression models (2019, background): “See Glynn and Quinn (2010) for an introduction to such methods in political science.”
The Statistics of Causal Inference: A View from Political Methodology (2015, positive): “See Gerber and Green (2012) for a detailed discussion of both topics.”
Doubly robust uniform confidence band for the conditional average treatment effect function (2016, methodology): “Doubly robust estimators have been advocated for use in many different areas of application: See, for example, Lunceford and Davidian (2004) for medicine, Glynn and Quinn (2010) for political science, Wooldridge (2010) for economics, and Schafer and Kang (2008) for psychology.”
The Generalized Oaxaca-Blinder Estimator (2020, methodology): “e generalized Oaxaca-Blinder estimator is algebraically equivalent to the the augmented inverse-propensity weighted (AIPW) treatment eect estimator with known (and constant) treatment propensity (seeGlynn and Quinn (2010) for an overview). In the survey sampling literature, that estimator is known a”