Implementing double-robust estimators of causal effects
This article describes the implementation of a double-robust estimator for pretest-posttest studies (Lunceford and Davidian, 2004, Statistics in Medicine 23: 2937-2960) and presents a new Stata command (dr) that carries out the procedure. A double-robust estimator gives the analyst two opportunities for obtaining unbiased inference when adjusting for selection eﬀects such as confounding by allowing for diﬀerent forms of model misspecification; a double-robust estimator also can offer increased efficiency when all the models are correctly speciﬁed. We demonstrate the results with a Monte Carlo simulation study, and we show how to implement the double-robust estimator on a single simulated dataset, both manually and by using the dr command. Copyright 2008 by StataCorp LP.
Volume (Year): 8 (2008)
Issue (Month): 3 (September)
|Contact details of provider:|| Web page: http://www.stata-journal.com/|
|Order Information:||Web: http://www.stata-journal.com/subscription.html|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.