Implementing double-robust estimators of causal effects

Richard Emsley (), Mark Lunt, Andrew Pickles and GraHam Dunn
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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

Stata Journal, 2008, vol. 8, issue 3, pages 334-353

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.

Keywords: dr; double-robust estimators; causal models; confounding; inverse probability of treatment weights; propensity score (search for similar items in EconPapers)
Date: 2008
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