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Implementing double-robust estimators of causal effects

Author

Listed:
  • Richard Emsley

    (Biostatistics, Health Methodology Research Group, The University of Manchester)

  • Mark Lunt

    (Arthritis Research Campaign Epidemiology Unit, The University of Manchester)

  • Andrew Pickles

    (Biostatistics, Health Methodology Research Group, The University of Manchester)

  • GraHam Dunn

    (Biostatistics, Health Methodology Research Group, The University of Manchester)

Abstract

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.

Suggested Citation

  • Richard Emsley & Mark Lunt & Andrew Pickles & GraHam Dunn, 2008. "Implementing double-robust estimators of causal effects," Stata Journal, StataCorp LP, vol. 8(3), pages 334-353, September.
    • Handle: RePEc:tsj:stataj:v:8:y:2008:i:3:p:334-353
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    References listed on IDEAS

    as
    1. 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.
    2. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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